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class="ds2-5-heading-sans-serif-sm">Sani Isah Abba</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://neu-tr.academia.edu/">Near East University</a>, <a class="u-tcGrayDarker" href="https://neu-tr.academia.edu/Departments/Civil_Engineering/Documents">Civil Engineering</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Sani" data-follow-user-id="25846730" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" 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href="https://www.academia.edu/116639398/A_Novel_Multi_model_Data_Driven_Ensemble_Technique_for_the_Prediction_of_Retention_Factor_in_HPLC_Method_Development"><img alt="Research paper thumbnail of A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development" class="work-thumbnail" src="https://attachments.academia-assets.com/112712779/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639398/A_Novel_Multi_model_Data_Driven_Ensemble_Technique_for_the_Prediction_of_Retention_Factor_in_HPLC_Method_Development">A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development</a></div><div class="wp-workCard_item"><span>Chromatographia</span><span>, Aug 1, 2020</span></div><div 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{"id":116639398,"title":"A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development","translated_title":"","metadata":{"publisher":"Springer Science+Business Media","grobid_abstract":"Reliable simulation of retention factor (k) is crucial in high-performance liquid chromatography (HPLC) method development. In this research, three different Artificial intelligence (AI) based models, namely the multi-layer perceptron (MLP), Support vector machine (SVM) and Hammerstein-Weiner (HW) models, were employed as well as three ensemble techniques, i.e., neural network ensemble (NNE), weighted average ensemble (WAE) and simple average ensemble (SAE) to predict k for HPLC method development. In this context, the pH and composition of the mobile phase (methanol) are used as the input variables with the corresponding Methyclothiazide (M) and Amiloride (A) as antihypertensive target analytes. The performance efficiency of the models was evaluated using mean square error (MSE), determination coefficient (R 2), and correlation coefficient (R). The results obtained from the single models showed that MLP outperformed the other two models and increased the prediction accuracy up to 1% and 3% for the HW and SVM models, respectively, for the prediction of M. However, for the prediction of A, SVM outperformed the other two models and increased the prediction accuracy up to 7% and 6% for HW and MLP, respectively. In the ensemble technique, the results obtained for the prediction of both M and A demonstrated that NNE increased the performance accuracy by 14% of the single models. Also, NNE proved to be superior to the two linear ensembles and improved the prediction accuracy up to 14% and 2% for SAE and WAE, respectively, for the simulation of M with R 2 = 0.9962 and 0.9949 for both calibration and verification, and up to 9% and 6% for A with R 2 = 0.9606 and 0.9569 for both calibration and verification phases respectively. The overall results depicted the reliability and robustness of both the AI-based models and justified the enhancement capability for ensemble techniques for both the two analytes.","publication_date":{"day":1,"month":8,"year":2020,"errors":{}},"publication_name":"Chromatographia","grobid_abstract_attachment_id":112712779},"translated_abstract":null,"internal_url":"https://www.academia.edu/116639398/A_Novel_Multi_model_Data_Driven_Ensemble_Technique_for_the_Prediction_of_Retention_Factor_in_HPLC_Method_Development","translated_internal_url":"","created_at":"2024-03-24T12:48:15.777-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712779,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712779/thumbnails/1.jpg","file_name":"s10337-020-03912-020240324-1-8uo984.pdf","download_url":"https://www.academia.edu/attachments/112712779/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Novel_Multi_model_Data_Driven_Ensemble.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712779/s10337-020-03912-020240324-1-8uo984-libre.pdf?1711315306=\u0026response-content-disposition=attachment%3B+filename%3DA_Novel_Multi_model_Data_Driven_Ensemble.pdf\u0026Expires=1732833493\u0026Signature=Qu6E7rOYeWzCfBImEUty7q79S77Fz3av2oykcFd72Juvf-INzUwzFfsLWZxTqNFc~PL7k16fbx8uiz-MFkO6TjDq3nXv2T4G9iFiFUkfMuiS6WuYNc1ttDorIKrUrKaVnztT83JsoE4EkSVCNbNW0qrlCdxC7OHxVSlYznmq1D7KXG78mq7cjx0gKdTRGruw5hSIrcgKpttY0cbkWJmhA3EO3S4VO9x9vvq-QGV6UGsByRT6~XS4ISIpZYYK9HbhkYyXn5kN80cT2SmHmWUkLyd8YsauXXgJBVYiKZL8tvD2nPM5Rt9Dy6RceTfJwU~VWZaK6gxRfffaBTjLEiKHqQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Novel_Multi_model_Data_Driven_Ensemble_Technique_for_the_Prediction_of_Retention_Factor_in_HPLC_Method_Development","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712779,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712779/thumbnails/1.jpg","file_name":"s10337-020-03912-020240324-1-8uo984.pdf","download_url":"https://www.academia.edu/attachments/112712779/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Novel_Multi_model_Data_Driven_Ensemble.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712779/s10337-020-03912-020240324-1-8uo984-libre.pdf?1711315306=\u0026response-content-disposition=attachment%3B+filename%3DA_Novel_Multi_model_Data_Driven_Ensemble.pdf\u0026Expires=1732833493\u0026Signature=Qu6E7rOYeWzCfBImEUty7q79S77Fz3av2oykcFd72Juvf-INzUwzFfsLWZxTqNFc~PL7k16fbx8uiz-MFkO6TjDq3nXv2T4G9iFiFUkfMuiS6WuYNc1ttDorIKrUrKaVnztT83JsoE4EkSVCNbNW0qrlCdxC7OHxVSlYznmq1D7KXG78mq7cjx0gKdTRGruw5hSIrcgKpttY0cbkWJmhA3EO3S4VO9x9vvq-QGV6UGsByRT6~XS4ISIpZYYK9HbhkYyXn5kN80cT2SmHmWUkLyd8YsauXXgJBVYiKZL8tvD2nPM5Rt9Dy6RceTfJwU~VWZaK6gxRfffaBTjLEiKHqQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4656,"name":"Chromatography","url":"https://www.academia.edu/Documents/in/Chromatography"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":225787,"name":"High Performance Liquid Chromatography","url":"https://www.academia.edu/Documents/in/High_Performance_Liquid_Chromatography"},{"id":238159,"name":"Multilayer Perceptron","url":"https://www.academia.edu/Documents/in/Multilayer_Perceptron"},{"id":511858,"name":"Ensemble Learning","url":"https://www.academia.edu/Documents/in/Ensemble_Learning"},{"id":611814,"name":"Correlation coefficient","url":"https://www.academia.edu/Documents/in/Correlation_coefficient"},{"id":875404,"name":"Retention Time","url":"https://www.academia.edu/Documents/in/Retention_Time"},{"id":997803,"name":"Ensemble Forecasting","url":"https://www.academia.edu/Documents/in/Ensemble_Forecasting"},{"id":1004747,"name":"Perceptron","url":"https://www.academia.edu/Documents/in/Perceptron"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":40576816,"url":"https://doi.org/10.1007/s10337-020-03912-0"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639397"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639397/Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction"><img alt="Research paper thumbnail of Experimental exploration of influential factors of concrete flexural strength through features engineering techniques: Insight from machine learning prediction" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639397/Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction">Experimental exploration of influential factors of concrete flexural strength through features engineering techniques: Insight from machine learning prediction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a res...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a result, this study used four different types of coarse aggregates, including those that depends on morphology, which contain coarse aggregates that can reach an extreme size of 20 mm and have the appearance of being flaky, angular, rounded, and irregular. The concrete mixtures were made based on Department of Environment (DoE) method of mix design, and a target FS of 5 MPa at 28 days was established. The FS of the concrete mixtures was assessed 7, 14, and 28 days after curing. In addition, the research employed Feedforward neural network (FFNN), Gaussian process regression (GPR), Multilinear Regression (MLR), and Stepwise Linear Regression (SWR) models in the prediction of concrete FS. The FFNN, GPR, MLR, and SWR models were trained on the investigational data obtained from the study&#39;s laboratory. The outcome showed that concrete with coarse aggregate in a round form had the maximum slu...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639397"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639397"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639397; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639397]").text(description); $(".js-view-count[data-work-id=116639397]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639397; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639397']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639397, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=116639397]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639397,"title":"Experimental exploration of influential factors of concrete flexural strength through features engineering techniques: Insight from machine learning prediction","translated_title":"","metadata":{"abstract":"The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a result, this study used four different types of coarse aggregates, including those that depends on morphology, which contain coarse aggregates that can reach an extreme size of 20 mm and have the appearance of being flaky, angular, rounded, and irregular. The concrete mixtures were made based on Department of Environment (DoE) method of mix design, and a target FS of 5 MPa at 28 days was established. The FS of the concrete mixtures was assessed 7, 14, and 28 days after curing. In addition, the research employed Feedforward neural network (FFNN), Gaussian process regression (GPR), Multilinear Regression (MLR), and Stepwise Linear Regression (SWR) models in the prediction of concrete FS. The FFNN, GPR, MLR, and SWR models were trained on the investigational data obtained from the study\u0026#39;s laboratory. The outcome showed that concrete with coarse aggregate in a round form had the maximum slu...","publisher":"Research Square Platform LLC"},"translated_abstract":"The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a result, this study used four different types of coarse aggregates, including those that depends on morphology, which contain coarse aggregates that can reach an extreme size of 20 mm and have the appearance of being flaky, angular, rounded, and irregular. The concrete mixtures were made based on Department of Environment (DoE) method of mix design, and a target FS of 5 MPa at 28 days was established. The FS of the concrete mixtures was assessed 7, 14, and 28 days after curing. In addition, the research employed Feedforward neural network (FFNN), Gaussian process regression (GPR), Multilinear Regression (MLR), and Stepwise Linear Regression (SWR) models in the prediction of concrete FS. The FFNN, GPR, MLR, and SWR models were trained on the investigational data obtained from the study\u0026#39;s laboratory. The outcome showed that concrete with coarse aggregate in a round form had the maximum slu...","internal_url":"https://www.academia.edu/116639397/Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction","translated_internal_url":"","created_at":"2024-03-24T12:48:15.611-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah 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of High-Performance Concrete" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639396/Implementation_of_Nonlinear_Computing_Models_and_Classical_Regression_for_Predicting_Compressive_Strength_of_High_Performance_Concrete">Implementation of Nonlinear Computing Models and Classical Regression for Predicting Compressive Strength of High-Performance Concrete</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639396"><a 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$('.js-work-strip[data-work-id=116639396]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639396,"title":"Implementation of Nonlinear Computing Models and Classical Regression for Predicting Compressive Strength of High-Performance Concrete","translated_title":"","metadata":{"publisher":"Elsevier 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paper thumbnail of Performance Evaluation of Hydroponic Wastewater Treatment Plant Integrated with Ensemble Learning Techniques: A Feature Selection Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/112712759/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639395/Performance_Evaluation_of_Hydroponic_Wastewater_Treatment_Plant_Integrated_with_Ensemble_Learning_Techniques_A_Feature_Selection_Approach">Performance Evaluation of Hydroponic Wastewater Treatment Plant Integrated with Ensemble Learning Techniques: A Feature Selection Approach</a></div><div class="wp-workCard_item"><span>Processes</span><span>, Feb 4, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a 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prediction of roadway traffic noise based on non-linear mutual information" class="work-thumbnail" src="https://attachments.academia-assets.com/112712777/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639394/An_intelligent_hybridized_computing_techniques_for_the_prediction_of_roadway_traffic_noise_based_on_non_linear_mutual_information">An intelligent hybridized computing techniques for the prediction of roadway traffic noise based on non-linear mutual information</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A reliable traffic noise prediction model is one of the decision-making tools used in providing a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. In this study, four linear-nonlinear hybrid models were proposed to capture both linear and nonlinear patterns of the data by summing up the predicted traffic noise from the multilinear regression (MLR) and estimated residuals from four artificial intelligence (AI)-based models. The input variables for the models were volumes of cars, medium vehicles, buses, heavy vehicles, and average speed. Prior to the development of the hybrid model, the potential of Boosted Regression Tree (BRT), Feed Forward Neural Network (FFNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and Linear regression models for traffic noise prediction were evaluated and compared with each other. The performances of the single and hybrid models were evaluated using the Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE) and relativ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="882e628926f93f29f4d2240c20999ded" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712777,"asset_id":116639394,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712777/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639394"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639394"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639394; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639394]").text(description); $(".js-view-count[data-work-id=116639394]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639394; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639394']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639394, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "882e628926f93f29f4d2240c20999ded" } } $('.js-work-strip[data-work-id=116639394]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639394,"title":"An intelligent hybridized computing techniques for the prediction of roadway traffic noise based on non-linear mutual information","translated_title":"","metadata":{"abstract":"A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. In this study, four linear-nonlinear hybrid models were proposed to capture both linear and nonlinear patterns of the data by summing up the predicted traffic noise from the multilinear regression (MLR) and estimated residuals from four artificial intelligence (AI)-based models. The input variables for the models were volumes of cars, medium vehicles, buses, heavy vehicles, and average speed. Prior to the development of the hybrid model, the potential of Boosted Regression Tree (BRT), Feed Forward Neural Network (FFNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and Linear regression models for traffic noise prediction were evaluated and compared with each other. The performances of the single and hybrid models were evaluated using the Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE) and relativ...","publisher":"Research Square Platform LLC"},"translated_abstract":"A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. In this study, four linear-nonlinear hybrid models were proposed to capture both linear and nonlinear patterns of the data by summing up the predicted traffic noise from the multilinear regression (MLR) and estimated residuals from four artificial intelligence (AI)-based models. The input variables for the models were volumes of cars, medium vehicles, buses, heavy vehicles, and average speed. Prior to the development of the hybrid model, the potential of Boosted Regression Tree (BRT), Feed Forward Neural Network (FFNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and Linear regression models for traffic noise prediction were evaluated and compared with each other. The performances of the single and hybrid models were evaluated using the Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE) and relativ...","internal_url":"https://www.academia.edu/116639394/An_intelligent_hybridized_computing_techniques_for_the_prediction_of_roadway_traffic_noise_based_on_non_linear_mutual_information","translated_internal_url":"","created_at":"2024-03-24T12:48:15.126-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712777,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712777/thumbnails/1.jpg","file_name":"latest.pdf","download_url":"https://www.academia.edu/attachments/112712777/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_intelligent_hybridized_computing_tech.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712777/latest-libre.pdf?1711315316=\u0026response-content-disposition=attachment%3B+filename%3DAn_intelligent_hybridized_computing_tech.pdf\u0026Expires=1732833493\u0026Signature=UDPqO3FTSiHDudBsnSGp2sSDwYgdTVlDCuDO8DOCXCGCSNjMPILlj3YuALzm6UP794VbOMMNTgfwWcA-EcQQC7m-aZtuwXySuzP-ozAOE403z8g13dzanA8bocPbMRb2cH0EYlljgzWmBLUj5HdjJokrEzmZ~7LyUJB1v3mvZiTafokiQ9kXM2Puh9G4aX2H5G461l4vSI9xVVcV~9RC4WDwXMVimNdHhCtSqlBeRIljz-Q7Izf186LbGCPcPB6HfP8v9Ps~LbrQQBzZKefigvD1mk9-GPBu0-WwQbAOQFqfdnxU8h0wd72kQaYAMbOk~Aem9TfG2iFcv7o3QXa45w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_intelligent_hybridized_computing_techniques_for_the_prediction_of_roadway_traffic_noise_based_on_non_linear_mutual_information","translated_slug":"","page_count":38,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712777,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712777/thumbnails/1.jpg","file_name":"latest.pdf","download_url":"https://www.academia.edu/attachments/112712777/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_intelligent_hybridized_computing_tech.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712777/latest-libre.pdf?1711315316=\u0026response-content-disposition=attachment%3B+filename%3DAn_intelligent_hybridized_computing_tech.pdf\u0026Expires=1732833493\u0026Signature=UDPqO3FTSiHDudBsnSGp2sSDwYgdTVlDCuDO8DOCXCGCSNjMPILlj3YuALzm6UP794VbOMMNTgfwWcA-EcQQC7m-aZtuwXySuzP-ozAOE403z8g13dzanA8bocPbMRb2cH0EYlljgzWmBLUj5HdjJokrEzmZ~7LyUJB1v3mvZiTafokiQ9kXM2Puh9G4aX2H5G461l4vSI9xVVcV~9RC4WDwXMVimNdHhCtSqlBeRIljz-Q7Izf186LbGCPcPB6HfP8v9Ps~LbrQQBzZKefigvD1mk9-GPBu0-WwQbAOQFqfdnxU8h0wd72kQaYAMbOk~Aem9TfG2iFcv7o3QXa45w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":146286,"name":"Kriging","url":"https://www.academia.edu/Documents/in/Kriging"},{"id":181597,"name":"Root-Mean Square Error","url":"https://www.academia.edu/Documents/in/Root-Mean_Square_Error"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":3209333,"name":"Mean Squared Error","url":"https://www.academia.edu/Documents/in/Mean_Squared_Error"}],"urls":[{"id":40576813,"url":"https://www.researchsquare.com/article/rs-837045/v1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639393"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639393/Coupling_TPACK_Instructional_Model_With_Computing_Artificial_Intelligence_Techniques_to_Determine_Technical_and_Vocational_Education_Teacher_s_Computer_and_ICT_Tools_Competence"><img alt="Research paper thumbnail of Coupling TPACK Instructional Model With Computing Artificial Intelligence Techniques to Determine Technical and Vocational Education Teacher’s Computer and ICT Tools Competence" class="work-thumbnail" src="https://attachments.academia-assets.com/112712776/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639393/Coupling_TPACK_Instructional_Model_With_Computing_Artificial_Intelligence_Techniques_to_Determine_Technical_and_Vocational_Education_Teacher_s_Computer_and_ICT_Tools_Competence">Coupling TPACK Instructional Model With Computing Artificial Intelligence Techniques to Determine Technical and Vocational Education Teacher’s Computer and ICT Tools Competence</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nowadays, emerging technologies have changed the places of work through computers and ICT tools, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for instructors used in teaching and learning, most educators can&#39;t incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor production. To tackle this issue, it is essential to develop the tech-nical and vocational education and training (TVET) system by determining the quality of TVE. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional technology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were administ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b784494cf5496945509d980a1eafe17d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712776,"asset_id":116639393,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712776/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639393"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639393"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639393; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639393]").text(description); $(".js-view-count[data-work-id=116639393]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639393; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639393']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639393, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b784494cf5496945509d980a1eafe17d" } } $('.js-work-strip[data-work-id=116639393]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639393,"title":"Coupling TPACK Instructional Model With Computing Artificial Intelligence Techniques to Determine Technical and Vocational Education Teacher’s Computer and ICT Tools Competence","translated_title":"","metadata":{"abstract":"Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for instructors used in teaching and learning, most educators can\u0026#39;t incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor production. To tackle this issue, it is essential to develop the tech-nical and vocational education and training (TVET) system by determining the quality of TVE. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional technology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were administ...","publisher":"MDPI AG"},"translated_abstract":"Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for instructors used in teaching and learning, most educators can\u0026#39;t incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor production. To tackle this issue, it is essential to develop the tech-nical and vocational education and training (TVET) system by determining the quality of TVE. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional technology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were administ...","internal_url":"https://www.academia.edu/116639393/Coupling_TPACK_Instructional_Model_With_Computing_Artificial_Intelligence_Techniques_to_Determine_Technical_and_Vocational_Education_Teacher_s_Computer_and_ICT_Tools_Competence","translated_internal_url":"","created_at":"2024-03-24T12:48:14.978-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712776,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712776/thumbnails/1.jpg","file_name":"download.pdf","download_url":"https://www.academia.edu/attachments/112712776/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Coupling_TPACK_Instructional_Model_With.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712776/download-libre.pdf?1711315330=\u0026response-content-disposition=attachment%3B+filename%3DCoupling_TPACK_Instructional_Model_With.pdf\u0026Expires=1732833493\u0026Signature=TJKuv5VLipAv2Kh-Z4MmswzfK5rzkyZAXeU7i2hhgCpntFVKgLHyuo5Wuh0g~SPRSA4~DsZ75XMCF8tWCTSlD8K25qUWGUkNrhqLGZ53NKtS~RH-eE~Qf~Bxjkvcm-ibdQfLSvZp483~pdVztfZtSOacF5tQxpI2s8Lqwl5c-2yVLmUCtnSCz5jeogX2WXTjaseIf8SNFWwZ2MTS3Q7IJdsuPNiy7YO2-4pCmDsgBK8IrMROjYcqM-3X2gJk4ivaObdx8j7tGvz3uOjI8e5KCa0qtM2OcyDNRmXWYZMZHJPVdVYQhFD6nRJPnFVHupE3cG3e6vfOA3n-nOKYon4Yyw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Coupling_TPACK_Instructional_Model_With_Computing_Artificial_Intelligence_Techniques_to_Determine_Technical_and_Vocational_Education_Teacher_s_Computer_and_ICT_Tools_Competence","translated_slug":"","page_count":23,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712776,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712776/thumbnails/1.jpg","file_name":"download.pdf","download_url":"https://www.academia.edu/attachments/112712776/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Coupling_TPACK_Instructional_Model_With.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712776/download-libre.pdf?1711315330=\u0026response-content-disposition=attachment%3B+filename%3DCoupling_TPACK_Instructional_Model_With.pdf\u0026Expires=1732833493\u0026Signature=TJKuv5VLipAv2Kh-Z4MmswzfK5rzkyZAXeU7i2hhgCpntFVKgLHyuo5Wuh0g~SPRSA4~DsZ75XMCF8tWCTSlD8K25qUWGUkNrhqLGZ53NKtS~RH-eE~Qf~Bxjkvcm-ibdQfLSvZp483~pdVztfZtSOacF5tQxpI2s8Lqwl5c-2yVLmUCtnSCz5jeogX2WXTjaseIf8SNFWwZ2MTS3Q7IJdsuPNiy7YO2-4pCmDsgBK8IrMROjYcqM-3X2gJk4ivaObdx8j7tGvz3uOjI8e5KCa0qtM2OcyDNRmXWYZMZHJPVdVYQhFD6nRJPnFVHupE3cG3e6vfOA3n-nOKYon4Yyw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2731,"name":"Mathematics Education","url":"https://www.academia.edu/Documents/in/Mathematics_Education"},{"id":17369,"name":"Vocational Education","url":"https://www.academia.edu/Documents/in/Vocational_Education"},{"id":96212,"name":"Information and Communications Technology","url":"https://www.academia.edu/Documents/in/Information_and_Communications_Technology"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639392"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639392/Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia"><img alt="Research paper thumbnail of Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia" class="work-thumbnail" src="https://attachments.academia-assets.com/112712758/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639392/Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia">Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia</a></div><div class="wp-workCard_item"><span>Molecules</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Unconsolidated earthen surface materials can retain heavy metals originating from different sourc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/k...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="404da7fcbb1287878f423ab23726c736" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712758,"asset_id":116639392,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712758/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639392"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639392"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639392; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639392]").text(description); $(".js-view-count[data-work-id=116639392]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639392; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639392']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639392, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "404da7fcbb1287878f423ab23726c736" } } $('.js-work-strip[data-work-id=116639392]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639392,"title":"Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia","translated_title":"","metadata":{"abstract":"Unconsolidated earthen surface materials can retain heavy metals originating from different sources. 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The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/k...","publisher":"MDPI AG","publication_name":"Molecules"},"translated_abstract":"Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/k...","internal_url":"https://www.academia.edu/116639392/Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia","translated_internal_url":"","created_at":"2024-03-24T12:48:14.767-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712758,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712758/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712758/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Geochemical_and_Spatial_Distribution_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712758/pdf-libre.pdf?1711315318=\u0026response-content-disposition=attachment%3B+filename%3DGeochemical_and_Spatial_Distribution_of.pdf\u0026Expires=1732833493\u0026Signature=YRd-2HrBYV1zR4thnekC0tNIJbFZ9PUxO61FfS~Zbs0SegWF3SsEmcIQRApNTucc~YAC04CzisAm6sHiVaZPloYMbSZg8ykm9Tn-FYm4tcfIzXvpCEZXrBefovKsnOfDdO7fla51ZE8sVhqah9Ncti9-4DTLM7580msdm3c-al5-JWg7ukEag14iVTFr~~bCT8PyFyUAVAxMq50Af7ZfaeT0csyVAbrvcEYTH7LWF~p5no60CNsvErhka3atG9d1sHULEX3y1AhyphEpobukrpCy1i61Wc0RWSGxWCoJ51RR-p6ivY5ogmhTbeqiZ3Vi1th2Wnz3WPjLXNfCkPzwPA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia","translated_slug":"","page_count":19,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712758,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712758/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712758/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Geochemical_and_Spatial_Distribution_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712758/pdf-libre.pdf?1711315318=\u0026response-content-disposition=attachment%3B+filename%3DGeochemical_and_Spatial_Distribution_of.pdf\u0026Expires=1732833493\u0026Signature=YRd-2HrBYV1zR4thnekC0tNIJbFZ9PUxO61FfS~Zbs0SegWF3SsEmcIQRApNTucc~YAC04CzisAm6sHiVaZPloYMbSZg8ykm9Tn-FYm4tcfIzXvpCEZXrBefovKsnOfDdO7fla51ZE8sVhqah9Ncti9-4DTLM7580msdm3c-al5-JWg7ukEag14iVTFr~~bCT8PyFyUAVAxMq50Af7ZfaeT0csyVAbrvcEYTH7LWF~p5no60CNsvErhka3atG9d1sHULEX3y1AhyphEpobukrpCy1i61Wc0RWSGxWCoJ51RR-p6ivY5ogmhTbeqiZ3Vi1th2Wnz3WPjLXNfCkPzwPA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":531,"name":"Organic Chemistry","url":"https://www.academia.edu/Documents/in/Organic_Chemistry"},{"id":4467,"name":"Chemometrics","url":"https://www.academia.edu/Documents/in/Chemometrics"},{"id":37159,"name":"Environmental Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Environmental_Analytical_Chemistry"},{"id":57697,"name":"Heavy metals","url":"https://www.academia.edu/Documents/in/Heavy_metals"},{"id":328449,"name":"Molecules","url":"https://www.academia.edu/Documents/in/Molecules"},{"id":793816,"name":"Topsoil","url":"https://www.academia.edu/Documents/in/Topsoil"}],"urls":[{"id":40576812,"url":"https://www.mdpi.com/1420-3049/27/13/4220/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639391"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639391/Multi_Regional_Modeling_of_Cumulative_COVID_19_Cases_Integrated_with_Environmental_Forest_Knowledge_Estimation_A_Deep_Learning_Ensemble_Approach"><img alt="Research paper thumbnail of Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/112712761/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639391/Multi_Regional_Modeling_of_Cumulative_COVID_19_Cases_Integrated_with_Environmental_Forest_Knowledge_Estimation_A_Deep_Learning_Ensemble_Approach">Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach</a></div><div class="wp-workCard_item"><span>International Journal of Environmental Research and Public Health</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the be...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c9855b41a58b8271be1a4b677d099d70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712761,"asset_id":116639391,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712761/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639391"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639391"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639391; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639391]").text(description); $(".js-view-count[data-work-id=116639391]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639391; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639391']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639391, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c9855b41a58b8271be1a4b677d099d70" } } $('.js-work-strip[data-work-id=116639391]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639391,"title":"Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach","translated_title":"","metadata":{"abstract":"Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the be...","publisher":"MDPI AG","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"International Journal of Environmental Research and Public Health"},"translated_abstract":"Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639390"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639390/Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_"><img alt="Research paper thumbnail of Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639390/Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_">Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)</a></div><div class="wp-workCard_item"><span>In Silico Pharmacology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article, three data-driven approaches were explored, including two artificial intelligenc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639390"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639390"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639390; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639390]").text(description); $(".js-view-count[data-work-id=116639390]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639390; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639390']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639390, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=116639390]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639390,"title":"Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)","translated_title":"","metadata":{"abstract":"In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"In Silico Pharmacology"},"translated_abstract":"In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.","internal_url":"https://www.academia.edu/116639390/Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_","translated_internal_url":"","created_at":"2024-03-24T12:48:14.387-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":3209333,"name":"Mean Squared Error","url":"https://www.academia.edu/Documents/in/Mean_Squared_Error"},{"id":3402456,"name":"Extreme Learning Machine","url":"https://www.academia.edu/Documents/in/Extreme_Learning_Machine"}],"urls":[{"id":40576810,"url":"https://link.springer.com/content/pdf/10.1007/s40203-021-00090-1.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639389"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639389/Adaptive_neuro_fuzzy_inference_system_coupled_with_shuffled_frog_leaping_algorithm_for_predicting_river_streamflow_time_series"><img alt="Research paper thumbnail of Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series" class="work-thumbnail" src="https://attachments.academia-assets.com/112712778/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639389/Adaptive_neuro_fuzzy_inference_system_coupled_with_shuffled_frog_leaping_algorithm_for_predicting_river_streamflow_time_series">Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series</a></div><div class="wp-workCard_item"><span>Hydrological Sciences Journal</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3d4a198bbbcab9020533932378f077a4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712778,"asset_id":116639389,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712778/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639389"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639389"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639389; 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In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input-output architecture were investigated. The results show that the proposed ANFIS-SFLA model (r = 0.88; NS = 0.88; RMSE = 141.39; MAE = 88.94; MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (r = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83; MAPE = 42.59%). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639388"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639388/Water_resources_management_and_trend_of_water_use_in_North_Cyprus"><img alt="Research paper thumbnail of Water resources management and trend of water use in North Cyprus" class="work-thumbnail" src="https://attachments.academia-assets.com/112712757/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639388/Water_resources_management_and_trend_of_water_use_in_North_Cyprus">Water resources management and trend of water use in North Cyprus</a></div><div class="wp-workCard_item"><span>DESALINATION AND WATER TREATMENT</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1afa3a2a802f277094dd37c073a7d507" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712757,"asset_id":116639388,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712757/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639388"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639388"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639388; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1afa3a2a802f277094dd37c073a7d507" } } $('.js-work-strip[data-work-id=116639388]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639388,"title":"Water resources management and trend of water use in North Cyprus","translated_title":"","metadata":{"publisher":"Desalination Publications","ai_title_tag":"Trends in Water Use and Management in North Cyprus","grobid_abstract":"North Cyprus has been experiencing water scarcity since 1960 due to the limited freshwater resources, climate impact, and high rate of evaporation. As there are no perennial rivers, the island is largely dependent on groundwater as the main source of supply. The gradual increase in water demand led to the excessive extraction of freshwater from aquifers; this caused seawater intrusion, thus making the scarcity more alarming. Hence, this research was conducted to provide an update on the trend of the water budget of the country. Data were collected between 2000 to 2012 from the relevant authorities and used to achieve the study objective. Statistical relationships and the Blaney-Criddle method were applied to process the data. It was found that domestic water demands increased from 35.9 to 50.4 MCM while conveyance losses declined from 55.8 to 24.2 MCM. Moreover, an assessment conducted for the agricultural economy of 21 groups of crops showed that about 146.7 and 115.1 million USD were generated in 2011 and 2012, respectively. The overall results implied that the trend of water demand for agricultural production fluctuates with time and the general trend of water use is negative owing to the modernization of irrigation systems that minimizes the losses.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"DESALINATION AND WATER TREATMENT","grobid_abstract_attachment_id":112712757},"translated_abstract":null,"internal_url":"https://www.academia.edu/116639388/Water_resources_management_and_trend_of_water_use_in_North_Cyprus","translated_internal_url":"","created_at":"2024-03-24T12:48:14.014-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712757/thumbnails/1.jpg","file_name":"177_2020_264.pdf","download_url":"https://www.academia.edu/attachments/112712757/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Water_resources_management_and_trend_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712757/177_2020_264-libre.pdf?1711315307=\u0026response-content-disposition=attachment%3B+filename%3DWater_resources_management_and_trend_of.pdf\u0026Expires=1732833493\u0026Signature=fdxwoq8O3nz6q5svAi2S5zfG8~XROkhe4nTmaL~EN7U6YXU-GhWoWHzvFGDfjDuZkcOY7dGbdAMyw794bkzSQjHSiBkALuhEaDAKSbiqt4HGSZGra-UDG6L3bT4g~Dt4sLCiV~cSEDLhOKEzU2~DvWqRNr0ADMACCTySrRzCrfT5xwRmfFDpMyPPLgw8nxtHhbxqXj8kLOnZkIc6bsEk5qX6xxMId70Krpo6W-dONrsB54vZ~cY~DAfFGhN2Qff4fY0g4fszrlh12cWaAYMuRaQ7fYZMMzkBf2cBqrYwr4MvUDkmwG5RnCtF4U1aHT8tJA2jERy-v0~Xwq6cfra90Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Water_resources_management_and_trend_of_water_use_in_North_Cyprus","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712757/thumbnails/1.jpg","file_name":"177_2020_264.pdf","download_url":"https://www.academia.edu/attachments/112712757/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Water_resources_management_and_trend_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712757/177_2020_264-libre.pdf?1711315307=\u0026response-content-disposition=attachment%3B+filename%3DWater_resources_management_and_trend_of.pdf\u0026Expires=1732833493\u0026Signature=fdxwoq8O3nz6q5svAi2S5zfG8~XROkhe4nTmaL~EN7U6YXU-GhWoWHzvFGDfjDuZkcOY7dGbdAMyw794bkzSQjHSiBkALuhEaDAKSbiqt4HGSZGra-UDG6L3bT4g~Dt4sLCiV~cSEDLhOKEzU2~DvWqRNr0ADMACCTySrRzCrfT5xwRmfFDpMyPPLgw8nxtHhbxqXj8kLOnZkIc6bsEk5qX6xxMId70Krpo6W-dONrsB54vZ~cY~DAfFGhN2Qff4fY0g4fszrlh12cWaAYMuRaQ7fYZMMzkBf2cBqrYwr4MvUDkmwG5RnCtF4U1aHT8tJA2jERy-v0~Xwq6cfra90Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":4526,"name":"Water resources","url":"https://www.academia.edu/Documents/in/Water_resources"},{"id":156376,"name":"Water resource management","url":"https://www.academia.edu/Documents/in/Water_resource_management"}],"urls":[{"id":40576808,"url":"http://www.deswater.com/DWT_articles/vol_177_papers/177_2020_264.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639386"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639386/Effluent_Water_Reuse_Possibilities_in_Northern_Cyprus"><img alt="Research paper thumbnail of Effluent Water Reuse Possibilities in Northern Cyprus" class="work-thumbnail" src="https://attachments.academia-assets.com/112712755/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639386/Effluent_Water_Reuse_Possibilities_in_Northern_Cyprus">Effluent Water Reuse Possibilities in Northern Cyprus</a></div><div class="wp-workCard_item"><span>Water</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition experiences due to global warming effects. Previous studies revealed that the water management policy in the country is not sustainable from the perspective of demand and balance. Apparently, the reuse of recycled water will be an alternative resource and can be utilized for some specific purposes to reduce water extraction from the ground. It is expected that treated wastewater will reach 20 million cubic meters (MCM) per year after the completion of the new sewage system for Lefkosa. Today, 20,000 m3 of wastewater is treated at the Lefkosa Central Treatment Plant up to the secondary treatment level, in which the degree of treatment varies from 60% to 95% owing to the weather conditions in the country during the year. Effluent water reuse in NC was not accepted due to cultural belief. However, water scarcity was experienced in the country during the last decade, forcing the farmers to be...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6b9070f48692f3f4e307a0872b9d9607" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712755,"asset_id":116639386,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712755/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639386"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639386"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639386; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639386]").text(description); $(".js-view-count[data-work-id=116639386]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639386; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639386']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639386, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6b9070f48692f3f4e307a0872b9d9607" } } $('.js-work-strip[data-work-id=116639386]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639386,"title":"Effluent Water Reuse Possibilities in Northern Cyprus","translated_title":"","metadata":{"abstract":"Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition experiences due to global warming effects. Previous studies revealed that the water management policy in the country is not sustainable from the perspective of demand and balance. Apparently, the reuse of recycled water will be an alternative resource and can be utilized for some specific purposes to reduce water extraction from the ground. It is expected that treated wastewater will reach 20 million cubic meters (MCM) per year after the completion of the new sewage system for Lefkosa. Today, 20,000 m3 of wastewater is treated at the Lefkosa Central Treatment Plant up to the secondary treatment level, in which the degree of treatment varies from 60% to 95% owing to the weather conditions in the country during the year. Effluent water reuse in NC was not accepted due to cultural belief. However, water scarcity was experienced in the country during the last decade, forcing the farmers to be...","publisher":"MDPI AG","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Water"},"translated_abstract":"Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition experiences due to global warming effects. Previous studies revealed that the water management policy in the country is not sustainable from the perspective of demand and balance. Apparently, the reuse of recycled water will be an alternative resource and can be utilized for some specific purposes to reduce water extraction from the ground. It is expected that treated wastewater will reach 20 million cubic meters (MCM) per year after the completion of the new sewage system for Lefkosa. Today, 20,000 m3 of wastewater is treated at the Lefkosa Central Treatment Plant up to the secondary treatment level, in which the degree of treatment varies from 60% to 95% owing to the weather conditions in the country during the year. Effluent water reuse in NC was not accepted due to cultural belief. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639385"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639385/Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble"><img alt="Research paper thumbnail of Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble" class="work-thumbnail" src="https://attachments.academia-assets.com/112712782/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639385/Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble">Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble</a></div><div class="wp-workCard_item"><span>Natural Resources Research</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3c17350c7ef96f229eb0ed7b4bd6fa2a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712782,"asset_id":116639385,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712782/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639385"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639385"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639385; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639385]").text(description); $(".js-view-count[data-work-id=116639385]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639385; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639385']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639385, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza-Varas, Hargreaves-Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature T max ð Þ, minimum temperature T min ð Þ, mean temperature T mean ð Þ, relative humidity R H ð Þ, and wind speed U 2 ð Þ were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures T max ; T mean ; T min ð Þ and relative humidity are the dominant parameters. While temperaturebased empirical models and MLR model could be employed to achieve reliable results, AIbased models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Natural Resources Research","grobid_abstract_attachment_id":112712782},"translated_abstract":null,"internal_url":"https://www.academia.edu/116639385/Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble","translated_internal_url":"","created_at":"2024-03-24T12:48:13.646-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712782,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712782/thumbnails/1.jpg","file_name":"s11053-018-09450-920240324-1-v9nih5.pdf","download_url":"https://www.academia.edu/attachments/112712782/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_region_Modeling_of_Daily_Global_So.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712782/s11053-018-09450-920240324-1-v9nih5-libre.pdf?1711315310=\u0026response-content-disposition=attachment%3B+filename%3DMulti_region_Modeling_of_Daily_Global_So.pdf\u0026Expires=1732833493\u0026Signature=aZmBHhVoAyY~JD90qMgasHR2goPtUMWZJ3KTH-bpR9dfjPeA6--1wli98Uug-EtqGiz54weBLDv8sBezyyOESQzRrkJbLAOnbkOzsfwn68lOQ-uaVlxUFWaEqlNhPCRDFzuJioTqhkclElJ3cYM7FCNj8bYARAGuBuwhQ5GqfNKpa~g4SSQIPeiB66Nr-Y7LS3T5WcGNMWc4gb6r5riqauREFyhmbP30TLtwUH3UTDgeVPGEZCm4N7FzdwZA2Y13E~YcMvdQE3nWIRxObUCFyKdLJar0U76CNfLkWEeSAaYXJvJaiosaIktjCW9ZY0WdrOPVAIPZDwsmv5rmXL5jHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble","translated_slug":"","page_count":22,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712782,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712782/thumbnails/1.jpg","file_name":"s11053-018-09450-920240324-1-v9nih5.pdf","download_url":"https://www.academia.edu/attachments/112712782/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_region_Modeling_of_Daily_Global_So.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712782/s11053-018-09450-920240324-1-v9nih5-libre.pdf?1711315310=\u0026response-content-disposition=attachment%3B+filename%3DMulti_region_Modeling_of_Daily_Global_So.pdf\u0026Expires=1732833493\u0026Signature=aZmBHhVoAyY~JD90qMgasHR2goPtUMWZJ3KTH-bpR9dfjPeA6--1wli98Uug-EtqGiz54weBLDv8sBezyyOESQzRrkJbLAOnbkOzsfwn68lOQ-uaVlxUFWaEqlNhPCRDFzuJioTqhkclElJ3cYM7FCNj8bYARAGuBuwhQ5GqfNKpa~g4SSQIPeiB66Nr-Y7LS3T5WcGNMWc4gb6r5riqauREFyhmbP30TLtwUH3UTDgeVPGEZCm4N7FzdwZA2Y13E~YcMvdQE3nWIRxObUCFyKdLJar0U76CNfLkWEeSAaYXJvJaiosaIktjCW9ZY0WdrOPVAIPZDwsmv5rmXL5jHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":2216,"name":"Natural Resources","url":"https://www.academia.edu/Documents/in/Natural_Resources"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":1290754,"name":"Sunshine Duration","url":"https://www.academia.edu/Documents/in/Sunshine_Duration"},{"id":1366660,"name":"Empirical Modelling","url":"https://www.academia.edu/Documents/in/Empirical_Modelling"},{"id":1957240,"name":"ENVIRONMENTAL SCIENCE AND MANAGEMENT","url":"https://www.academia.edu/Documents/in/ENVIRONMENTAL_SCIENCE_AND_MANAGEMENT"}],"urls":[{"id":40576806,"url":"http://link.springer.com/content/pdf/10.1007/s11053-018-09450-9.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639350"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639350/Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites"><img alt="Research paper thumbnail of Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites" class="work-thumbnail" src="https://attachments.academia-assets.com/112712721/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639350/Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites">Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites</a></div><div class="wp-workCard_item"><span>Applied Sciences</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study presents multi-response optimization and prediction tribological behaviors polytetrafl...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influenc...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c70b374bddc4cb3324fc192ca5958478" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712721,"asset_id":116639350,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712721/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639350"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639350"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639350; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639350]").text(description); $(".js-view-count[data-work-id=116639350]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639350; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639350']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639350, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c70b374bddc4cb3324fc192ca5958478" } } $('.js-work-strip[data-work-id=116639350]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639350,"title":"Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites","translated_title":"","metadata":{"abstract":"This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influenc...","publisher":"MDPI AG","publication_name":"Applied Sciences"},"translated_abstract":"This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influenc...","internal_url":"https://www.academia.edu/116639350/Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites","translated_internal_url":"","created_at":"2024-03-24T12:46:55.478-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712721,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712721/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712721/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Artificial_Intelligence_Models_wi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712721/pdf-libre.pdf?1711315319=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Artificial_Intelligence_Models_wi.pdf\u0026Expires=1732833493\u0026Signature=bM9j9AEIiz~gBVDXkgL2tMJ9w9FG5YZa2JkydnZ2y9gsMo7SrlVLbi31HJJCTPudyEjp9gsC0hS~-QNwks4-LepNqbM7G48cAFjF~bhZ6n41IrmrcchhcZr43BrAGlI9b4yJgK1HqWtvf79~HVK3dthkLc~mVULfU30MVsO9xDWgdhee00lkHzFTPOGWP7rtBR3Wa6K7LJiYX6cj~ol~3kl4curMnD1ZiUOZ3IEDb8g8M-TrAhlprP9LdGyRG08DGvkQGuxtKS4zIaVsXQTpuSqaVo8v-yVjrJY3v1DMAVzFAwrjoeF7ebMgUHTgJ5tf8R9EpixxxH3hKiO8VRQkuA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites","translated_slug":"","page_count":26,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712721,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712721/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712721/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Artificial_Intelligence_Models_wi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712721/pdf-libre.pdf?1711315319=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Artificial_Intelligence_Models_wi.pdf\u0026Expires=1732833493\u0026Signature=bM9j9AEIiz~gBVDXkgL2tMJ9w9FG5YZa2JkydnZ2y9gsMo7SrlVLbi31HJJCTPudyEjp9gsC0hS~-QNwks4-LepNqbM7G48cAFjF~bhZ6n41IrmrcchhcZr43BrAGlI9b4yJgK1HqWtvf79~HVK3dthkLc~mVULfU30MVsO9xDWgdhee00lkHzFTPOGWP7rtBR3Wa6K7LJiYX6cj~ol~3kl4curMnD1ZiUOZ3IEDb8g8M-TrAhlprP9LdGyRG08DGvkQGuxtKS4zIaVsXQTpuSqaVo8v-yVjrJY3v1DMAVzFAwrjoeF7ebMgUHTgJ5tf8R9EpixxxH3hKiO8VRQkuA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":112712722,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712722/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712722/download_file","bulk_download_file_name":"Hybrid_Artificial_Intelligence_Models_wi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712722/pdf-libre.pdf?1711315329=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Artificial_Intelligence_Models_wi.pdf\u0026Expires=1732833493\u0026Signature=Os8rEZiPD1HjS3rluTWqIJRy7-jHE539hBzex83Om7KT~sFVYD-cS288k7FqAhL~RS-mqckEddKEBR4OcWyS3gAqji1me8eBKzr6OqUKY3VvFKK9JW-d2mx2JALiYSTTw3ZJPhe6L087b9fB3s2nxNXL-YtUksgjbC10iM1BLOsJdPncNIcDbg~3Bfm9NoCqf3qfjfUUgZvAT~oQx4Oe2MljOfrwV5Cx-O1cKdAY3cBbbdHnUFVda8nKkCHo8zdLpXyVeRJQoeUJ1~Gc0JWnGAL4ngXn0aXbd24fituaZ~Gkxq4K8E16epee9OPl38kaG9cZUYMVbPy~mq3vpbmsfg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":23032,"name":"Tribology","url":"https://www.academia.edu/Documents/in/Tribology"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization"},{"id":89183,"name":"Taguchi Methods","url":"https://www.academia.edu/Documents/in/Taguchi_Methods"},{"id":159232,"name":"Applied Sciences","url":"https://www.academia.edu/Documents/in/Applied_Sciences"},{"id":3209333,"name":"Mean Squared Error","url":"https://www.academia.edu/Documents/in/Mean_Squared_Error"}],"urls":[{"id":40576780,"url":"https://www.mdpi.com/2076-3417/12/17/8671/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639349"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639349/Optimization_and_prediction_of_tribological_behaviour_of_filled_polytetrafluoroethylene_composites_using_Taguchi_Deng_and_hybrid_support_vector_regression_models"><img alt="Research paper thumbnail of Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models" class="work-thumbnail" src="https://attachments.academia-assets.com/112712720/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639349/Optimization_and_prediction_of_tribological_behaviour_of_filled_polytetrafluoroethylene_composites_using_Taguchi_Deng_and_hybrid_support_vector_regression_models">Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models</a></div><div class="wp-workCard_item"><span>Scientific Reports</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study presents optimization and prediction of tribological behaviour of filled polytetrafluo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses.To predict µ and Ks, SVR was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model eva...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="53752ae264953e7e40d59f2888c98894" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712720,"asset_id":116639349,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712720/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639349"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639349"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639349; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639349]").text(description); $(".js-view-count[data-work-id=116639349]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639349; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639349']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639349, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "53752ae264953e7e40d59f2888c98894" } } $('.js-work-strip[data-work-id=116639349]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639349,"title":"Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models","translated_title":"","metadata":{"abstract":"This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses.To predict µ and Ks, SVR was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model eva...","publisher":"Springer Science and Business Media LLC","publication_name":"Scientific Reports"},"translated_abstract":"This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses.To predict µ and Ks, SVR was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model eva...","internal_url":"https://www.academia.edu/116639349/Optimization_and_prediction_of_tribological_behaviour_of_filled_polytetrafluoroethylene_composites_using_Taguchi_Deng_and_hybrid_support_vector_regression_models","translated_internal_url":"","created_at":"2024-03-24T12:46:55.325-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712720,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712720/thumbnails/1.jpg","file_name":"s41598-022-14629-5.pdf","download_url":"https://www.academia.edu/attachments/112712720/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Optimization_and_prediction_of_tribologi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712720/s41598-022-14629-5-libre.pdf?1711315319=\u0026response-content-disposition=attachment%3B+filename%3DOptimization_and_prediction_of_tribologi.pdf\u0026Expires=1732833493\u0026Signature=epNa1URXWxunwWWr6UzTHQCAJ8dfDE~TXP~tEpkKo6g8oKIrDV1~IaGlhQ3bbkEGoA5I7MH-9ooLFWJcr5T43X94wQlhanKwito7wJVB7cgCVKvaSEiDYgEXdcRFVBcPaYEU6gJnf5Ld1~C8-nyD6XZq3o8XxHX6C0WK8Llyi-QirHQSq8w5fbNERz~IOls4bj3qu32hv3vz4HhW9D18mjdrY6C7eRL~FbNH1Jq5dtpyPm6BYYl1EnbxMxk28MoZ-bzXuU8VOHr0rv5RyrrCo9rWusYV8A-0Z0pjImOWYv1ErqXR1zxnEkFmFtWaqxcjI0B3ojsf3ycFK6R1jFtxEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Optimization_and_prediction_of_tribological_behaviour_of_filled_polytetrafluoroethylene_composites_using_Taguchi_Deng_and_hybrid_support_vector_regression_models","translated_slug":"","page_count":22,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712720,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712720/thumbnails/1.jpg","file_name":"s41598-022-14629-5.pdf","download_url":"https://www.academia.edu/attachments/112712720/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Optimization_and_prediction_of_tribologi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712720/s41598-022-14629-5-libre.pdf?1711315319=\u0026response-content-disposition=attachment%3B+filename%3DOptimization_and_prediction_of_tribologi.pdf\u0026Expires=1732833493\u0026Signature=epNa1URXWxunwWWr6UzTHQCAJ8dfDE~TXP~tEpkKo6g8oKIrDV1~IaGlhQ3bbkEGoA5I7MH-9ooLFWJcr5T43X94wQlhanKwito7wJVB7cgCVKvaSEiDYgEXdcRFVBcPaYEU6gJnf5Ld1~C8-nyD6XZq3o8XxHX6C0WK8Llyi-QirHQSq8w5fbNERz~IOls4bj3qu32hv3vz4HhW9D18mjdrY6C7eRL~FbNH1Jq5dtpyPm6BYYl1EnbxMxk28MoZ-bzXuU8VOHr0rv5RyrrCo9rWusYV8A-0Z0pjImOWYv1ErqXR1zxnEkFmFtWaqxcjI0B3ojsf3ycFK6R1jFtxEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":112712719,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712719/thumbnails/1.jpg","file_name":"s41598-022-14629-5.pdf","download_url":"https://www.academia.edu/attachments/112712719/download_file","bulk_download_file_name":"Optimization_and_prediction_of_tribologi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712719/s41598-022-14629-5-libre.pdf?1711315321=\u0026response-content-disposition=attachment%3B+filename%3DOptimization_and_prediction_of_tribologi.pdf\u0026Expires=1732833493\u0026Signature=Rmx~DiDcLVKt~p8PiKRfpoAccMVu0aZjrDUIIzdGOHb~6vOyVxMzs7f-grnCI6VB4V15bPwUa6qqgH6ZOXkBEJxxAy3bUrpw8mhrsrLO~pTNco~dtE3Qzi~fD8EFOAfy7G0C~F1I-JWwheWaA5EXFlBVE8QWNGBfIEHJnIWKy4MvHlOrh9YdQB89sqGHmtbMmfRSTYYwOZwymENzqgteG7-YDqIOuBdnAnxxi9vBTbQUjQosBRex4DMI8JemHS9eggKNkyg1SfVEGsaw0vTItp0TkssClKzTKgzbRr-d1ZaRqDAEyYsaPlhcdXJYo196VIKZPWTlYwawH7MwZO99Wg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":23032,"name":"Tribology","url":"https://www.academia.edu/Documents/in/Tribology"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization"},{"id":89183,"name":"Taguchi Methods","url":"https://www.academia.edu/Documents/in/Taguchi_Methods"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":289812,"name":"Orthogonal Array","url":"https://www.academia.edu/Documents/in/Orthogonal_Array"}],"urls":[{"id":40576779,"url":"https://www.nature.com/articles/s41598-022-14629-5.pdf"}]}, dispatcherData: dispatcherData }); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98903022]").text(description); $(".js-view-count[data-work-id=98903022]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98903022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98903022']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 98903022, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a1f2f55a87d388be0089f5171b70381e" } } $('.js-work-strip[data-work-id=98903022]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98903022,"title":"Assessment of climate change impact on probable maximum floods in a tropical catchment","translated_title":"","metadata":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"Increased extreme rainfall due to climate change will increase the probable maximum ood (PMF) and pose a severe threat the critical hydraulic infrastructure like hydroelectric and ood protection dams. As the rainfall extremes in tropical regions are highly sensitive to global warming, increase PMF can be much higher in the tropics. A study has been conducted to assess the impact of climate change on PMF in a tropical catchment located in peninsular Malaysia. A lumped hydrological model, Mike NAM, is calibrated and validated with observed climate and in ow data of Tenmengor reservoir, located in the state of Perak of Peninsular Malaysia. Regional climate model projected rainfall is used to generate probable maximum precipitation (PMP) for future periods. The hydrological model is used to simulate PMF from PMP estimated for the historical and two future periods, early (2031−2045) and late (2060−2075). The results revealed the NAM model could simulate the river ow with a Nash-Sutcliffe e ciency of 0.74 and root mean square error of 0.51. The application of the model with projected rainfall revealed an increase in PMP by 162 to 507% and 259 to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating oods in the study area due to climate change.","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Theoretical and Applied Climatology","grobid_abstract_attachment_id":100130703},"translated_abstract":null,"internal_url":"https://www.academia.edu/98903022/Assessment_of_climate_change_impact_on_probable_maximum_floods_in_a_tropical_catchment","translated_internal_url":"","created_at":"2023-03-21T09:00:40.994-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":100130703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100130703/thumbnails/1.jpg","file_name":"latest.pdf","download_url":"https://www.academia.edu/attachments/100130703/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessment_of_climate_change_impact_on_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100130703/latest-libre.pdf?1679415405=\u0026response-content-disposition=attachment%3B+filename%3DAssessment_of_climate_change_impact_on_p.pdf\u0026Expires=1732833493\u0026Signature=Mh6gGuUhvV36wgFTQ7nZvputX6Ck1p5dGvqRMgMZGYWgGM19~0CWMTTeDPZd3DejqnCjcFNythDvMUfkx3fabgKfQgimE4Q5~6COT89nVoLDhcBqL4si1mYznAHxomkMl5UiOWw4xZ5FszK99513S3muci8RySV41sNqIvhalfkgLIA-LUeuBiYAG8Qij4quIvCaHPE3bvStUMXkbIAqLf04ZhnAa7VgbBR2UqLFgbTd49YmezsxmoxZzM8Ptdtcr3O~pl7V4s560XVmJqBCh7qF2cdj2ETnFib7yguk6dl9tBaPsiTpgt3fUhUCEdIId8Wd7n9wVWsU9KIsq5Le5Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessment_of_climate_change_impact_on_probable_maximum_floods_in_a_tropical_catchment","translated_slug":"","page_count":23,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":100130703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100130703/thumbnails/1.jpg","file_name":"latest.pdf","download_url":"https://www.academia.edu/attachments/100130703/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessment_of_climate_change_impact_on_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100130703/latest-libre.pdf?1679415405=\u0026response-content-disposition=attachment%3B+filename%3DAssessment_of_climate_change_impact_on_p.pdf\u0026Expires=1732833493\u0026Signature=Mh6gGuUhvV36wgFTQ7nZvputX6Ck1p5dGvqRMgMZGYWgGM19~0CWMTTeDPZd3DejqnCjcFNythDvMUfkx3fabgKfQgimE4Q5~6COT89nVoLDhcBqL4si1mYznAHxomkMl5UiOWw4xZ5FszK99513S3muci8RySV41sNqIvhalfkgLIA-LUeuBiYAG8Qij4quIvCaHPE3bvStUMXkbIAqLf04ZhnAa7VgbBR2UqLFgbTd49YmezsxmoxZzM8Ptdtcr3O~pl7V4s560XVmJqBCh7qF2cdj2ETnFib7yguk6dl9tBaPsiTpgt3fUhUCEdIId8Wd7n9wVWsU9KIsq5Le5Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1512,"name":"Climate Change","url":"https://www.academia.edu/Documents/in/Climate_Change"},{"id":3754,"name":"Climatology","url":"https://www.academia.edu/Documents/in/Climatology"},{"id":60285,"name":"Atmospheric sciences","url":"https://www.academia.edu/Documents/in/Atmospheric_sciences"},{"id":291658,"name":"Precipitation","url":"https://www.academia.edu/Documents/in/Precipitation"},{"id":2007066,"name":"Return period","url":"https://www.academia.edu/Documents/in/Return_period"},{"id":3647976,"name":"Flood Myth","url":"https://www.academia.edu/Documents/in/Flood_Myth"},{"id":4134089,"name":"Drainage basin","url":"https://www.academia.edu/Documents/in/Drainage_basin"}],"urls":[{"id":29975120,"url":"https://link.springer.com/content/pdf/10.1007/s00704-022-03925-9.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="8845282" id="papers"><div class="js-work-strip profile--work_container" data-work-id="116639398"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639398/A_Novel_Multi_model_Data_Driven_Ensemble_Technique_for_the_Prediction_of_Retention_Factor_in_HPLC_Method_Development"><img alt="Research paper thumbnail of A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development" class="work-thumbnail" src="https://attachments.academia-assets.com/112712779/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639398/A_Novel_Multi_model_Data_Driven_Ensemble_Technique_for_the_Prediction_of_Retention_Factor_in_HPLC_Method_Development">A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development</a></div><div class="wp-workCard_item"><span>Chromatographia</span><span>, Aug 1, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f14c6385b96629757bd63538a1108d91" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712779,"asset_id":116639398,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712779/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639398"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639398"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639398; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639398]").text(description); $(".js-view-count[data-work-id=116639398]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639398; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639398']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639398, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f14c6385b96629757bd63538a1108d91" } } $('.js-work-strip[data-work-id=116639398]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639398,"title":"A Novel Multi-model Data-Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development","translated_title":"","metadata":{"publisher":"Springer Science+Business Media","grobid_abstract":"Reliable simulation of retention factor (k) is crucial in high-performance liquid chromatography (HPLC) method development. In this research, three different Artificial intelligence (AI) based models, namely the multi-layer perceptron (MLP), Support vector machine (SVM) and Hammerstein-Weiner (HW) models, were employed as well as three ensemble techniques, i.e., neural network ensemble (NNE), weighted average ensemble (WAE) and simple average ensemble (SAE) to predict k for HPLC method development. In this context, the pH and composition of the mobile phase (methanol) are used as the input variables with the corresponding Methyclothiazide (M) and Amiloride (A) as antihypertensive target analytes. The performance efficiency of the models was evaluated using mean square error (MSE), determination coefficient (R 2), and correlation coefficient (R). The results obtained from the single models showed that MLP outperformed the other two models and increased the prediction accuracy up to 1% and 3% for the HW and SVM models, respectively, for the prediction of M. However, for the prediction of A, SVM outperformed the other two models and increased the prediction accuracy up to 7% and 6% for HW and MLP, respectively. In the ensemble technique, the results obtained for the prediction of both M and A demonstrated that NNE increased the performance accuracy by 14% of the single models. Also, NNE proved to be superior to the two linear ensembles and improved the prediction accuracy up to 14% and 2% for SAE and WAE, respectively, for the simulation of M with R 2 = 0.9962 and 0.9949 for both calibration and verification, and up to 9% and 6% for A with R 2 = 0.9606 and 0.9569 for both calibration and verification phases respectively. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639397"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639397/Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction"><img alt="Research paper thumbnail of Experimental exploration of influential factors of concrete flexural strength through features engineering techniques: Insight from machine learning prediction" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639397/Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction">Experimental exploration of influential factors of concrete flexural strength through features engineering techniques: Insight from machine learning prediction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a res...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a result, this study used four different types of coarse aggregates, including those that depends on morphology, which contain coarse aggregates that can reach an extreme size of 20 mm and have the appearance of being flaky, angular, rounded, and irregular. The concrete mixtures were made based on Department of Environment (DoE) method of mix design, and a target FS of 5 MPa at 28 days was established. The FS of the concrete mixtures was assessed 7, 14, and 28 days after curing. In addition, the research employed Feedforward neural network (FFNN), Gaussian process regression (GPR), Multilinear Regression (MLR), and Stepwise Linear Regression (SWR) models in the prediction of concrete FS. The FFNN, GPR, MLR, and SWR models were trained on the investigational data obtained from the study&#39;s laboratory. The outcome showed that concrete with coarse aggregate in a round form had the maximum slu...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639397"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639397"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639397; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639397]").text(description); $(".js-view-count[data-work-id=116639397]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639397; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639397']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639397, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=116639397]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639397,"title":"Experimental exploration of influential factors of concrete flexural strength through features engineering techniques: Insight from machine learning prediction","translated_title":"","metadata":{"abstract":"The kind and quality of coarse aggregate have a direct impact on flexural strength (FS). As a result, this study used four different types of coarse aggregates, including those that depends on morphology, which contain coarse aggregates that can reach an extreme size of 20 mm and have the appearance of being flaky, angular, rounded, and irregular. The concrete mixtures were made based on Department of Environment (DoE) method of mix design, and a target FS of 5 MPa at 28 days was established. The FS of the concrete mixtures was assessed 7, 14, and 28 days after curing. In addition, the research employed Feedforward neural network (FFNN), Gaussian process regression (GPR), Multilinear Regression (MLR), and Stepwise Linear Regression (SWR) models in the prediction of concrete FS. The FFNN, GPR, MLR, and SWR models were trained on the investigational data obtained from the study\u0026#39;s laboratory. 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The FFNN, GPR, MLR, and SWR models were trained on the investigational data obtained from the study\u0026#39;s laboratory. The outcome showed that concrete with coarse aggregate in a round form had the maximum slu...","internal_url":"https://www.academia.edu/116639397/Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction","translated_internal_url":"","created_at":"2024-03-24T12:48:15.611-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Experimental_exploration_of_influential_factors_of_concrete_flexural_strength_through_features_engineering_techniques_Insight_from_machine_learning_prediction","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah 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prediction of roadway traffic noise based on non-linear mutual information" class="work-thumbnail" src="https://attachments.academia-assets.com/112712777/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639394/An_intelligent_hybridized_computing_techniques_for_the_prediction_of_roadway_traffic_noise_based_on_non_linear_mutual_information">An intelligent hybridized computing techniques for the prediction of roadway traffic noise based on non-linear mutual information</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A reliable traffic noise prediction model is one of the decision-making tools used in providing a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. In this study, four linear-nonlinear hybrid models were proposed to capture both linear and nonlinear patterns of the data by summing up the predicted traffic noise from the multilinear regression (MLR) and estimated residuals from four artificial intelligence (AI)-based models. The input variables for the models were volumes of cars, medium vehicles, buses, heavy vehicles, and average speed. Prior to the development of the hybrid model, the potential of Boosted Regression Tree (BRT), Feed Forward Neural Network (FFNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and Linear regression models for traffic noise prediction were evaluated and compared with each other. The performances of the single and hybrid models were evaluated using the Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE) and relativ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="882e628926f93f29f4d2240c20999ded" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712777,"asset_id":116639394,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712777/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639394"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639394"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639394; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639394]").text(description); $(".js-view-count[data-work-id=116639394]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639394; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639394']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639394, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "882e628926f93f29f4d2240c20999ded" } } $('.js-work-strip[data-work-id=116639394]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639394,"title":"An intelligent hybridized computing techniques for the prediction of roadway traffic noise based on non-linear mutual information","translated_title":"","metadata":{"abstract":"A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639393"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639393/Coupling_TPACK_Instructional_Model_With_Computing_Artificial_Intelligence_Techniques_to_Determine_Technical_and_Vocational_Education_Teacher_s_Computer_and_ICT_Tools_Competence"><img alt="Research paper thumbnail of Coupling TPACK Instructional Model With Computing Artificial Intelligence Techniques to Determine Technical and Vocational Education Teacher’s Computer and ICT Tools Competence" class="work-thumbnail" src="https://attachments.academia-assets.com/112712776/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639393/Coupling_TPACK_Instructional_Model_With_Computing_Artificial_Intelligence_Techniques_to_Determine_Technical_and_Vocational_Education_Teacher_s_Computer_and_ICT_Tools_Competence">Coupling TPACK Instructional Model With Computing Artificial Intelligence Techniques to Determine Technical and Vocational Education Teacher’s Computer and ICT Tools Competence</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nowadays, emerging technologies have changed the places of work through computers and ICT tools, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for instructors used in teaching and learning, most educators can&#39;t incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor production. To tackle this issue, it is essential to develop the tech-nical and vocational education and training (TVET) system by determining the quality of TVE. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional technology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were administ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b784494cf5496945509d980a1eafe17d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712776,"asset_id":116639393,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712776/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639393"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639393"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639393; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639393]").text(description); $(".js-view-count[data-work-id=116639393]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639393; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639393']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639393, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b784494cf5496945509d980a1eafe17d" } } $('.js-work-strip[data-work-id=116639393]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639393,"title":"Coupling TPACK Instructional Model With Computing Artificial Intelligence Techniques to Determine Technical and Vocational Education Teacher’s Computer and ICT Tools Competence","translated_title":"","metadata":{"abstract":"Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639392"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639392/Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia"><img alt="Research paper thumbnail of Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia" class="work-thumbnail" src="https://attachments.academia-assets.com/112712758/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639392/Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia">Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia</a></div><div class="wp-workCard_item"><span>Molecules</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Unconsolidated earthen surface materials can retain heavy metals originating from different sourc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/k...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="404da7fcbb1287878f423ab23726c736" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712758,"asset_id":116639392,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712758/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639392"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639392"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639392; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639392]").text(description); $(".js-view-count[data-work-id=116639392]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639392; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639392']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639392, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "404da7fcbb1287878f423ab23726c736" } } $('.js-work-strip[data-work-id=116639392]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639392,"title":"Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia","translated_title":"","metadata":{"abstract":"Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/k...","publisher":"MDPI AG","publication_name":"Molecules"},"translated_abstract":"Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/k...","internal_url":"https://www.academia.edu/116639392/Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia","translated_internal_url":"","created_at":"2024-03-24T12:48:14.767-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712758,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712758/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712758/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Geochemical_and_Spatial_Distribution_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712758/pdf-libre.pdf?1711315318=\u0026response-content-disposition=attachment%3B+filename%3DGeochemical_and_Spatial_Distribution_of.pdf\u0026Expires=1732833493\u0026Signature=YRd-2HrBYV1zR4thnekC0tNIJbFZ9PUxO61FfS~Zbs0SegWF3SsEmcIQRApNTucc~YAC04CzisAm6sHiVaZPloYMbSZg8ykm9Tn-FYm4tcfIzXvpCEZXrBefovKsnOfDdO7fla51ZE8sVhqah9Ncti9-4DTLM7580msdm3c-al5-JWg7ukEag14iVTFr~~bCT8PyFyUAVAxMq50Af7ZfaeT0csyVAbrvcEYTH7LWF~p5no60CNsvErhka3atG9d1sHULEX3y1AhyphEpobukrpCy1i61Wc0RWSGxWCoJ51RR-p6ivY5ogmhTbeqiZ3Vi1th2Wnz3WPjLXNfCkPzwPA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Geochemical_and_Spatial_Distribution_of_Topsoil_HMs_Coupled_with_Modeling_of_Cr_Using_Chemometrics_Intelligent_Techniques_Case_Study_from_Dammam_Area_Saudi_Arabia","translated_slug":"","page_count":19,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712758,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712758/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712758/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Geochemical_and_Spatial_Distribution_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712758/pdf-libre.pdf?1711315318=\u0026response-content-disposition=attachment%3B+filename%3DGeochemical_and_Spatial_Distribution_of.pdf\u0026Expires=1732833493\u0026Signature=YRd-2HrBYV1zR4thnekC0tNIJbFZ9PUxO61FfS~Zbs0SegWF3SsEmcIQRApNTucc~YAC04CzisAm6sHiVaZPloYMbSZg8ykm9Tn-FYm4tcfIzXvpCEZXrBefovKsnOfDdO7fla51ZE8sVhqah9Ncti9-4DTLM7580msdm3c-al5-JWg7ukEag14iVTFr~~bCT8PyFyUAVAxMq50Af7ZfaeT0csyVAbrvcEYTH7LWF~p5no60CNsvErhka3atG9d1sHULEX3y1AhyphEpobukrpCy1i61Wc0RWSGxWCoJ51RR-p6ivY5ogmhTbeqiZ3Vi1th2Wnz3WPjLXNfCkPzwPA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":531,"name":"Organic Chemistry","url":"https://www.academia.edu/Documents/in/Organic_Chemistry"},{"id":4467,"name":"Chemometrics","url":"https://www.academia.edu/Documents/in/Chemometrics"},{"id":37159,"name":"Environmental Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Environmental_Analytical_Chemistry"},{"id":57697,"name":"Heavy metals","url":"https://www.academia.edu/Documents/in/Heavy_metals"},{"id":328449,"name":"Molecules","url":"https://www.academia.edu/Documents/in/Molecules"},{"id":793816,"name":"Topsoil","url":"https://www.academia.edu/Documents/in/Topsoil"}],"urls":[{"id":40576812,"url":"https://www.mdpi.com/1420-3049/27/13/4220/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639391"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639391/Multi_Regional_Modeling_of_Cumulative_COVID_19_Cases_Integrated_with_Environmental_Forest_Knowledge_Estimation_A_Deep_Learning_Ensemble_Approach"><img alt="Research paper thumbnail of Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/112712761/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639391/Multi_Regional_Modeling_of_Cumulative_COVID_19_Cases_Integrated_with_Environmental_Forest_Knowledge_Estimation_A_Deep_Learning_Ensemble_Approach">Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach</a></div><div class="wp-workCard_item"><span>International Journal of Environmental Research and Public Health</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the be...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c9855b41a58b8271be1a4b677d099d70" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712761,"asset_id":116639391,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712761/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639391"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639391"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639391; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639391]").text(description); $(".js-view-count[data-work-id=116639391]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639391; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639391']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639391, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c9855b41a58b8271be1a4b677d099d70" } } $('.js-work-strip[data-work-id=116639391]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639391,"title":"Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach","translated_title":"","metadata":{"abstract":"Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639390"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639390/Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_"><img alt="Research paper thumbnail of Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639390/Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_">Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)</a></div><div class="wp-workCard_item"><span>In Silico Pharmacology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article, three data-driven approaches were explored, including two artificial intelligenc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639390"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639390"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639390; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639390]").text(description); $(".js-view-count[data-work-id=116639390]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639390; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639390']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639390, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=116639390]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639390,"title":"Comparative performance of extreme learning machine and Hammerstein–Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)","translated_title":"","metadata":{"abstract":"In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"In Silico Pharmacology"},"translated_abstract":"In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.","internal_url":"https://www.academia.edu/116639390/Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_","translated_internal_url":"","created_at":"2024-03-24T12:48:14.387-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Comparative_performance_of_extreme_learning_machine_and_Hammerstein_Weiner_models_for_modelling_the_intestinal_hyper_motility_and_secretory_inhibitory_effects_of_methanolic_leaf_extract_of_Combretumhypopilinum_Diels_Combretaceae_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":3209333,"name":"Mean Squared Error","url":"https://www.academia.edu/Documents/in/Mean_Squared_Error"},{"id":3402456,"name":"Extreme Learning Machine","url":"https://www.academia.edu/Documents/in/Extreme_Learning_Machine"}],"urls":[{"id":40576810,"url":"https://link.springer.com/content/pdf/10.1007/s40203-021-00090-1.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639389"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639389/Adaptive_neuro_fuzzy_inference_system_coupled_with_shuffled_frog_leaping_algorithm_for_predicting_river_streamflow_time_series"><img alt="Research paper thumbnail of Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series" class="work-thumbnail" src="https://attachments.academia-assets.com/112712778/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639389/Adaptive_neuro_fuzzy_inference_system_coupled_with_shuffled_frog_leaping_algorithm_for_predicting_river_streamflow_time_series">Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series</a></div><div class="wp-workCard_item"><span>Hydrological Sciences Journal</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3d4a198bbbcab9020533932378f077a4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712778,"asset_id":116639389,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712778/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639389"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639389"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639389; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639389]").text(description); $(".js-view-count[data-work-id=116639389]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639389; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639389']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639389, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3d4a198bbbcab9020533932378f077a4" } } $('.js-work-strip[data-work-id=116639389]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639389,"title":"Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series","translated_title":"","metadata":{"publisher":"Informa UK Limited","grobid_abstract":"strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input-output architecture were investigated. The results show that the proposed ANFIS-SFLA model (r = 0.88; NS = 0.88; RMSE = 141.39; MAE = 88.94; MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (r = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83; MAPE = 42.59%). The proposed model could be generalized and applied in different rivers worldwide.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Hydrological Sciences Journal","grobid_abstract_attachment_id":112712778},"translated_abstract":null,"internal_url":"https://www.academia.edu/116639389/Adaptive_neuro_fuzzy_inference_system_coupled_with_shuffled_frog_leaping_algorithm_for_predicting_river_streamflow_time_series","translated_internal_url":"","created_at":"2024-03-24T12:48:14.220-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712778,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712778/thumbnails/1.jpg","file_name":"02626667.2020.175870320240324-1-u7w4iz.pdf","download_url":"https://www.academia.edu/attachments/112712778/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Adaptive_neuro_fuzzy_inference_system_co.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712778/02626667.2020.175870320240324-1-u7w4iz-libre.pdf?1711315311=\u0026response-content-disposition=attachment%3B+filename%3DAdaptive_neuro_fuzzy_inference_system_co.pdf\u0026Expires=1732833493\u0026Signature=ZGzN8A-JsAqm2O1rCSl7EWg3gHEnq4VZT5JzmCUTt7Y52tVZ4Ujn4v9P3RigjI~il6eLbwGOJbABi1Dle0ydUP3uwp7E28krTNiwGUoo0ZhMSnKR8juCFm-CYgvMBmwvTdFi4ms5s8jLHuM1Bm-jF2KD9fHBPcASyklzyFl3P3i7jdA9NZQrvBNPminRpfcvPqHq0q4XR~06X2mny2gokKsVa4hsOtuNRIt64c6c7wrEcOAgtfQuQxJU6yTFBUUy9jFtHkJMSyKy35IpfMphtoILLye78KIgoVejFrsfD4lD6fMpEDJoe6mvjFnyjuDM~VI9nhJPoC22NLPfT-BfOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Adaptive_neuro_fuzzy_inference_system_coupled_with_shuffled_frog_leaping_algorithm_for_predicting_river_streamflow_time_series","translated_slug":"","page_count":46,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712778,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712778/thumbnails/1.jpg","file_name":"02626667.2020.175870320240324-1-u7w4iz.pdf","download_url":"https://www.academia.edu/attachments/112712778/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Adaptive_neuro_fuzzy_inference_system_co.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712778/02626667.2020.175870320240324-1-u7w4iz-libre.pdf?1711315311=\u0026response-content-disposition=attachment%3B+filename%3DAdaptive_neuro_fuzzy_inference_system_co.pdf\u0026Expires=1732833493\u0026Signature=ZGzN8A-JsAqm2O1rCSl7EWg3gHEnq4VZT5JzmCUTt7Y52tVZ4Ujn4v9P3RigjI~il6eLbwGOJbABi1Dle0ydUP3uwp7E28krTNiwGUoo0ZhMSnKR8juCFm-CYgvMBmwvTdFi4ms5s8jLHuM1Bm-jF2KD9fHBPcASyklzyFl3P3i7jdA9NZQrvBNPminRpfcvPqHq0q4XR~06X2mny2gokKsVa4hsOtuNRIt64c6c7wrEcOAgtfQuQxJU6yTFBUUy9jFtHkJMSyKy35IpfMphtoILLye78KIgoVejFrsfD4lD6fMpEDJoe6mvjFnyjuDM~VI9nhJPoC22NLPfT-BfOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":55,"name":"Environmental Engineering","url":"https://www.academia.edu/Documents/in/Environmental_Engineering"},{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":136554,"name":"adaptive neuro fuzzy inference system (ANFIS)","url":"https://www.academia.edu/Documents/in/adaptive_neuro_fuzzy_inference_system_ANFIS_"},{"id":142811,"name":"Streamflow","url":"https://www.academia.edu/Documents/in/Streamflow"}],"urls":[{"id":40576809,"url":"https://www.tandfonline.com/doi/pdf/10.1080/02626667.2020.1758703"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639388"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639388/Water_resources_management_and_trend_of_water_use_in_North_Cyprus"><img alt="Research paper thumbnail of Water resources management and trend of water use in North Cyprus" class="work-thumbnail" src="https://attachments.academia-assets.com/112712757/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639388/Water_resources_management_and_trend_of_water_use_in_North_Cyprus">Water resources management and trend of water use in North Cyprus</a></div><div class="wp-workCard_item"><span>DESALINATION AND WATER TREATMENT</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1afa3a2a802f277094dd37c073a7d507" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712757,"asset_id":116639388,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712757/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639388"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639388"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639388; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639388]").text(description); $(".js-view-count[data-work-id=116639388]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639388; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639388']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639388, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1afa3a2a802f277094dd37c073a7d507" } } $('.js-work-strip[data-work-id=116639388]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639388,"title":"Water resources management and trend of water use in North Cyprus","translated_title":"","metadata":{"publisher":"Desalination Publications","ai_title_tag":"Trends in Water Use and Management in North Cyprus","grobid_abstract":"North Cyprus has been experiencing water scarcity since 1960 due to the limited freshwater resources, climate impact, and high rate of evaporation. As there are no perennial rivers, the island is largely dependent on groundwater as the main source of supply. The gradual increase in water demand led to the excessive extraction of freshwater from aquifers; this caused seawater intrusion, thus making the scarcity more alarming. Hence, this research was conducted to provide an update on the trend of the water budget of the country. Data were collected between 2000 to 2012 from the relevant authorities and used to achieve the study objective. Statistical relationships and the Blaney-Criddle method were applied to process the data. It was found that domestic water demands increased from 35.9 to 50.4 MCM while conveyance losses declined from 55.8 to 24.2 MCM. Moreover, an assessment conducted for the agricultural economy of 21 groups of crops showed that about 146.7 and 115.1 million USD were generated in 2011 and 2012, respectively. The overall results implied that the trend of water demand for agricultural production fluctuates with time and the general trend of water use is negative owing to the modernization of irrigation systems that minimizes the losses.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"DESALINATION AND WATER TREATMENT","grobid_abstract_attachment_id":112712757},"translated_abstract":null,"internal_url":"https://www.academia.edu/116639388/Water_resources_management_and_trend_of_water_use_in_North_Cyprus","translated_internal_url":"","created_at":"2024-03-24T12:48:14.014-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712757/thumbnails/1.jpg","file_name":"177_2020_264.pdf","download_url":"https://www.academia.edu/attachments/112712757/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Water_resources_management_and_trend_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712757/177_2020_264-libre.pdf?1711315307=\u0026response-content-disposition=attachment%3B+filename%3DWater_resources_management_and_trend_of.pdf\u0026Expires=1732833493\u0026Signature=fdxwoq8O3nz6q5svAi2S5zfG8~XROkhe4nTmaL~EN7U6YXU-GhWoWHzvFGDfjDuZkcOY7dGbdAMyw794bkzSQjHSiBkALuhEaDAKSbiqt4HGSZGra-UDG6L3bT4g~Dt4sLCiV~cSEDLhOKEzU2~DvWqRNr0ADMACCTySrRzCrfT5xwRmfFDpMyPPLgw8nxtHhbxqXj8kLOnZkIc6bsEk5qX6xxMId70Krpo6W-dONrsB54vZ~cY~DAfFGhN2Qff4fY0g4fszrlh12cWaAYMuRaQ7fYZMMzkBf2cBqrYwr4MvUDkmwG5RnCtF4U1aHT8tJA2jERy-v0~Xwq6cfra90Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Water_resources_management_and_trend_of_water_use_in_North_Cyprus","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712757/thumbnails/1.jpg","file_name":"177_2020_264.pdf","download_url":"https://www.academia.edu/attachments/112712757/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Water_resources_management_and_trend_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712757/177_2020_264-libre.pdf?1711315307=\u0026response-content-disposition=attachment%3B+filename%3DWater_resources_management_and_trend_of.pdf\u0026Expires=1732833493\u0026Signature=fdxwoq8O3nz6q5svAi2S5zfG8~XROkhe4nTmaL~EN7U6YXU-GhWoWHzvFGDfjDuZkcOY7dGbdAMyw794bkzSQjHSiBkALuhEaDAKSbiqt4HGSZGra-UDG6L3bT4g~Dt4sLCiV~cSEDLhOKEzU2~DvWqRNr0ADMACCTySrRzCrfT5xwRmfFDpMyPPLgw8nxtHhbxqXj8kLOnZkIc6bsEk5qX6xxMId70Krpo6W-dONrsB54vZ~cY~DAfFGhN2Qff4fY0g4fszrlh12cWaAYMuRaQ7fYZMMzkBf2cBqrYwr4MvUDkmwG5RnCtF4U1aHT8tJA2jERy-v0~Xwq6cfra90Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":4526,"name":"Water resources","url":"https://www.academia.edu/Documents/in/Water_resources"},{"id":156376,"name":"Water resource management","url":"https://www.academia.edu/Documents/in/Water_resource_management"}],"urls":[{"id":40576808,"url":"http://www.deswater.com/DWT_articles/vol_177_papers/177_2020_264.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639386"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639386/Effluent_Water_Reuse_Possibilities_in_Northern_Cyprus"><img alt="Research paper thumbnail of Effluent Water Reuse Possibilities in Northern Cyprus" class="work-thumbnail" src="https://attachments.academia-assets.com/112712755/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639386/Effluent_Water_Reuse_Possibilities_in_Northern_Cyprus">Effluent Water Reuse Possibilities in Northern Cyprus</a></div><div class="wp-workCard_item"><span>Water</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition experiences due to global warming effects. Previous studies revealed that the water management policy in the country is not sustainable from the perspective of demand and balance. Apparently, the reuse of recycled water will be an alternative resource and can be utilized for some specific purposes to reduce water extraction from the ground. It is expected that treated wastewater will reach 20 million cubic meters (MCM) per year after the completion of the new sewage system for Lefkosa. Today, 20,000 m3 of wastewater is treated at the Lefkosa Central Treatment Plant up to the secondary treatment level, in which the degree of treatment varies from 60% to 95% owing to the weather conditions in the country during the year. Effluent water reuse in NC was not accepted due to cultural belief. However, water scarcity was experienced in the country during the last decade, forcing the farmers to be...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6b9070f48692f3f4e307a0872b9d9607" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712755,"asset_id":116639386,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712755/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639386"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639386"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639386; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639386]").text(description); $(".js-view-count[data-work-id=116639386]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639386; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639386']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639386, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6b9070f48692f3f4e307a0872b9d9607" } } $('.js-work-strip[data-work-id=116639386]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639386,"title":"Effluent Water Reuse Possibilities in Northern Cyprus","translated_title":"","metadata":{"abstract":"Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition experiences due to global warming effects. Previous studies revealed that the water management policy in the country is not sustainable from the perspective of demand and balance. Apparently, the reuse of recycled water will be an alternative resource and can be utilized for some specific purposes to reduce water extraction from the ground. It is expected that treated wastewater will reach 20 million cubic meters (MCM) per year after the completion of the new sewage system for Lefkosa. Today, 20,000 m3 of wastewater is treated at the Lefkosa Central Treatment Plant up to the secondary treatment level, in which the degree of treatment varies from 60% to 95% owing to the weather conditions in the country during the year. Effluent water reuse in NC was not accepted due to cultural belief. However, water scarcity was experienced in the country during the last decade, forcing the farmers to be...","publisher":"MDPI AG","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Water"},"translated_abstract":"Northern Cyprus (NC) is suffering from limited water resources and reiterated drought condition experiences due to global warming effects. Previous studies revealed that the water management policy in the country is not sustainable from the perspective of demand and balance. Apparently, the reuse of recycled water will be an alternative resource and can be utilized for some specific purposes to reduce water extraction from the ground. It is expected that treated wastewater will reach 20 million cubic meters (MCM) per year after the completion of the new sewage system for Lefkosa. Today, 20,000 m3 of wastewater is treated at the Lefkosa Central Treatment Plant up to the secondary treatment level, in which the degree of treatment varies from 60% to 95% owing to the weather conditions in the country during the year. Effluent water reuse in NC was not accepted due to cultural belief. However, water scarcity was experienced in the country during the last decade, forcing the farmers to be...","internal_url":"https://www.academia.edu/116639386/Effluent_Water_Reuse_Possibilities_in_Northern_Cyprus","translated_internal_url":"","created_at":"2024-03-24T12:48:13.813-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712755,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712755/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712755/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Effluent_Water_Reuse_Possibilities_in_No.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712755/pdf-libre.pdf?1711315312=\u0026response-content-disposition=attachment%3B+filename%3DEffluent_Water_Reuse_Possibilities_in_No.pdf\u0026Expires=1732833493\u0026Signature=UN88TOE8OgTxPumxlvL5YdSSwug5vSZ5Ja-vA2wpIG-Qqd3hCcrlW-DtZOntl54Y0sjQoADvTNYsRz4eGSbgHqb4z1S3~I7Co2UEmCSbAmdRJW-AJA-kVRRp7lqNSK5a9Q-PxzK4YT0Xmt-wzaSe46UYmYC80ChJimFWHyhDFe77x0raudSRc3l85Z4eU02qlGeuLNX45TqCbZFxA5JEWVWR8D5vaIxNkCsgUArr1c7YY-6nUvD2kJ-GPpeMUgLvdlR55pOAkR7RbgpzVaOgf01Hw0jvuJRQKjmJmj6rjqH98Q1FlFdkhpNGgOt1b-JWOxJJRtScWmlH1SS6WW-OOg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Effluent_Water_Reuse_Possibilities_in_Northern_Cyprus","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712755,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712755/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712755/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Effluent_Water_Reuse_Possibilities_in_No.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712755/pdf-libre.pdf?1711315312=\u0026response-content-disposition=attachment%3B+filename%3DEffluent_Water_Reuse_Possibilities_in_No.pdf\u0026Expires=1732833493\u0026Signature=UN88TOE8OgTxPumxlvL5YdSSwug5vSZ5Ja-vA2wpIG-Qqd3hCcrlW-DtZOntl54Y0sjQoADvTNYsRz4eGSbgHqb4z1S3~I7Co2UEmCSbAmdRJW-AJA-kVRRp7lqNSK5a9Q-PxzK4YT0Xmt-wzaSe46UYmYC80ChJimFWHyhDFe77x0raudSRc3l85Z4eU02qlGeuLNX45TqCbZFxA5JEWVWR8D5vaIxNkCsgUArr1c7YY-6nUvD2kJ-GPpeMUgLvdlR55pOAkR7RbgpzVaOgf01Hw0jvuJRQKjmJmj6rjqH98Q1FlFdkhpNGgOt1b-JWOxJJRtScWmlH1SS6WW-OOg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":112712756,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712756/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712756/download_file","bulk_download_file_name":"Effluent_Water_Reuse_Possibilities_in_No.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712756/pdf-libre.pdf?1711315302=\u0026response-content-disposition=attachment%3B+filename%3DEffluent_Water_Reuse_Possibilities_in_No.pdf\u0026Expires=1732833493\u0026Signature=ZpWbKbdtH97~~CWD2zGCzCkQwRnfHM0zDctNiVVMViorpZKuuVnhGM2L7UjmJYC4V2gcR2~1DSqrv1VOipklntQVPpHtjSXn24BvMIlLO9mC8Bh~4R0tTIeewueoOoxmJPgKwUeyfAIR9ts5g~xXXiHyEfP8Eskpzu6joSDVVYdXJmlzNdUz~AFYUt0ocVyHQ0xq4UBGyJqkCn0siE8RliOgu-OcvdppmdMuMHKJIwBS~icj29Dq6urj7WKZqza0W8xWf20qEWrIsUnO8cEtxS6CcmsVkSQlfwLf457wiEr~rCYojktnp2aScnrrKo3z5BkYzroanJuNBCkLIJVFew__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":2215,"name":"Water","url":"https://www.academia.edu/Documents/in/Water"},{"id":4526,"name":"Water resources","url":"https://www.academia.edu/Documents/in/Water_resources"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":65757,"name":"Wastewater","url":"https://www.academia.edu/Documents/in/Wastewater"},{"id":177350,"name":"Reuse","url":"https://www.academia.edu/Documents/in/Reuse"},{"id":551896,"name":"Sewage Treatment","url":"https://www.academia.edu/Documents/in/Sewage_Treatment"},{"id":602119,"name":"Effluent","url":"https://www.academia.edu/Documents/in/Effluent"}],"urls":[{"id":40576807,"url":"http://www.mdpi.com/2073-4441/11/2/191/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639385"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639385/Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble"><img alt="Research paper thumbnail of Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble" class="work-thumbnail" src="https://attachments.academia-assets.com/112712782/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639385/Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble">Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble</a></div><div class="wp-workCard_item"><span>Natural Resources Research</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3c17350c7ef96f229eb0ed7b4bd6fa2a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712782,"asset_id":116639385,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712782/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639385"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639385"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639385; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639385]").text(description); $(".js-view-count[data-work-id=116639385]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639385; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639385']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639385, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3c17350c7ef96f229eb0ed7b4bd6fa2a" } } $('.js-work-strip[data-work-id=116639385]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639385,"title":"Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble","translated_title":"","metadata":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"Solar radiation data are crucial for the design and evaluation of solar energy systems, climatic studies, water resources management, estimating crop productivity, etc. As so, for locations where direct measurements are not available, reliable models may be developed to estimate solar radiation from more readily available data. In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza-Varas, Hargreaves-Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature T max ð Þ, minimum temperature T min ð Þ, mean temperature T mean ð Þ, relative humidity R H ð Þ, and wind speed U 2 ð Þ were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures T max ; T mean ; T min ð Þ and relative humidity are the dominant parameters. While temperaturebased empirical models and MLR model could be employed to achieve reliable results, AIbased models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Natural Resources Research","grobid_abstract_attachment_id":112712782},"translated_abstract":null,"internal_url":"https://www.academia.edu/116639385/Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble","translated_internal_url":"","created_at":"2024-03-24T12:48:13.646-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712782,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712782/thumbnails/1.jpg","file_name":"s11053-018-09450-920240324-1-v9nih5.pdf","download_url":"https://www.academia.edu/attachments/112712782/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_region_Modeling_of_Daily_Global_So.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712782/s11053-018-09450-920240324-1-v9nih5-libre.pdf?1711315310=\u0026response-content-disposition=attachment%3B+filename%3DMulti_region_Modeling_of_Daily_Global_So.pdf\u0026Expires=1732833493\u0026Signature=aZmBHhVoAyY~JD90qMgasHR2goPtUMWZJ3KTH-bpR9dfjPeA6--1wli98Uug-EtqGiz54weBLDv8sBezyyOESQzRrkJbLAOnbkOzsfwn68lOQ-uaVlxUFWaEqlNhPCRDFzuJioTqhkclElJ3cYM7FCNj8bYARAGuBuwhQ5GqfNKpa~g4SSQIPeiB66Nr-Y7LS3T5WcGNMWc4gb6r5riqauREFyhmbP30TLtwUH3UTDgeVPGEZCm4N7FzdwZA2Y13E~YcMvdQE3nWIRxObUCFyKdLJar0U76CNfLkWEeSAaYXJvJaiosaIktjCW9ZY0WdrOPVAIPZDwsmv5rmXL5jHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multi_region_Modeling_of_Daily_Global_Solar_Radiation_with_Artificial_Intelligence_Ensemble","translated_slug":"","page_count":22,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712782,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712782/thumbnails/1.jpg","file_name":"s11053-018-09450-920240324-1-v9nih5.pdf","download_url":"https://www.academia.edu/attachments/112712782/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_region_Modeling_of_Daily_Global_So.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712782/s11053-018-09450-920240324-1-v9nih5-libre.pdf?1711315310=\u0026response-content-disposition=attachment%3B+filename%3DMulti_region_Modeling_of_Daily_Global_So.pdf\u0026Expires=1732833493\u0026Signature=aZmBHhVoAyY~JD90qMgasHR2goPtUMWZJ3KTH-bpR9dfjPeA6--1wli98Uug-EtqGiz54weBLDv8sBezyyOESQzRrkJbLAOnbkOzsfwn68lOQ-uaVlxUFWaEqlNhPCRDFzuJioTqhkclElJ3cYM7FCNj8bYARAGuBuwhQ5GqfNKpa~g4SSQIPeiB66Nr-Y7LS3T5WcGNMWc4gb6r5riqauREFyhmbP30TLtwUH3UTDgeVPGEZCm4N7FzdwZA2Y13E~YcMvdQE3nWIRxObUCFyKdLJar0U76CNfLkWEeSAaYXJvJaiosaIktjCW9ZY0WdrOPVAIPZDwsmv5rmXL5jHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":2216,"name":"Natural Resources","url":"https://www.academia.edu/Documents/in/Natural_Resources"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":1290754,"name":"Sunshine Duration","url":"https://www.academia.edu/Documents/in/Sunshine_Duration"},{"id":1366660,"name":"Empirical Modelling","url":"https://www.academia.edu/Documents/in/Empirical_Modelling"},{"id":1957240,"name":"ENVIRONMENTAL SCIENCE AND MANAGEMENT","url":"https://www.academia.edu/Documents/in/ENVIRONMENTAL_SCIENCE_AND_MANAGEMENT"}],"urls":[{"id":40576806,"url":"http://link.springer.com/content/pdf/10.1007/s11053-018-09450-9.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639350"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639350/Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites"><img alt="Research paper thumbnail of Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites" class="work-thumbnail" src="https://attachments.academia-assets.com/112712721/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639350/Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites">Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites</a></div><div class="wp-workCard_item"><span>Applied Sciences</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study presents multi-response optimization and prediction tribological behaviors polytetrafl...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influenc...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c70b374bddc4cb3324fc192ca5958478" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112712721,"asset_id":116639350,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112712721/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116639350"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116639350"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116639350; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116639350]").text(description); $(".js-view-count[data-work-id=116639350]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116639350; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116639350']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116639350, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c70b374bddc4cb3324fc192ca5958478" } } $('.js-work-strip[data-work-id=116639350]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116639350,"title":"Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites","translated_title":"","metadata":{"abstract":"This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influenc...","publisher":"MDPI AG","publication_name":"Applied Sciences"},"translated_abstract":"This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influenc...","internal_url":"https://www.academia.edu/116639350/Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites","translated_internal_url":"","created_at":"2024-03-24T12:46:55.478-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":112712721,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712721/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712721/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Artificial_Intelligence_Models_wi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712721/pdf-libre.pdf?1711315319=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Artificial_Intelligence_Models_wi.pdf\u0026Expires=1732833493\u0026Signature=bM9j9AEIiz~gBVDXkgL2tMJ9w9FG5YZa2JkydnZ2y9gsMo7SrlVLbi31HJJCTPudyEjp9gsC0hS~-QNwks4-LepNqbM7G48cAFjF~bhZ6n41IrmrcchhcZr43BrAGlI9b4yJgK1HqWtvf79~HVK3dthkLc~mVULfU30MVsO9xDWgdhee00lkHzFTPOGWP7rtBR3Wa6K7LJiYX6cj~ol~3kl4curMnD1ZiUOZ3IEDb8g8M-TrAhlprP9LdGyRG08DGvkQGuxtKS4zIaVsXQTpuSqaVo8v-yVjrJY3v1DMAVzFAwrjoeF7ebMgUHTgJ5tf8R9EpixxxH3hKiO8VRQkuA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hybrid_Artificial_Intelligence_Models_with_Multi_Objective_Optimization_for_Prediction_of_Tribological_Behavior_of_Polytetrafluoroethylene_Matrix_Composites","translated_slug":"","page_count":26,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":112712721,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712721/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712721/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Hybrid_Artificial_Intelligence_Models_wi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712721/pdf-libre.pdf?1711315319=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Artificial_Intelligence_Models_wi.pdf\u0026Expires=1732833493\u0026Signature=bM9j9AEIiz~gBVDXkgL2tMJ9w9FG5YZa2JkydnZ2y9gsMo7SrlVLbi31HJJCTPudyEjp9gsC0hS~-QNwks4-LepNqbM7G48cAFjF~bhZ6n41IrmrcchhcZr43BrAGlI9b4yJgK1HqWtvf79~HVK3dthkLc~mVULfU30MVsO9xDWgdhee00lkHzFTPOGWP7rtBR3Wa6K7LJiYX6cj~ol~3kl4curMnD1ZiUOZ3IEDb8g8M-TrAhlprP9LdGyRG08DGvkQGuxtKS4zIaVsXQTpuSqaVo8v-yVjrJY3v1DMAVzFAwrjoeF7ebMgUHTgJ5tf8R9EpixxxH3hKiO8VRQkuA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":112712722,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112712722/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/112712722/download_file","bulk_download_file_name":"Hybrid_Artificial_Intelligence_Models_wi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112712722/pdf-libre.pdf?1711315329=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Artificial_Intelligence_Models_wi.pdf\u0026Expires=1732833493\u0026Signature=Os8rEZiPD1HjS3rluTWqIJRy7-jHE539hBzex83Om7KT~sFVYD-cS288k7FqAhL~RS-mqckEddKEBR4OcWyS3gAqji1me8eBKzr6OqUKY3VvFKK9JW-d2mx2JALiYSTTw3ZJPhe6L087b9fB3s2nxNXL-YtUksgjbC10iM1BLOsJdPncNIcDbg~3Bfm9NoCqf3qfjfUUgZvAT~oQx4Oe2MljOfrwV5Cx-O1cKdAY3cBbbdHnUFVda8nKkCHo8zdLpXyVeRJQoeUJ1~Gc0JWnGAL4ngXn0aXbd24fituaZ~Gkxq4K8E16epee9OPl38kaG9cZUYMVbPy~mq3vpbmsfg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":23032,"name":"Tribology","url":"https://www.academia.edu/Documents/in/Tribology"},{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization"},{"id":89183,"name":"Taguchi Methods","url":"https://www.academia.edu/Documents/in/Taguchi_Methods"},{"id":159232,"name":"Applied Sciences","url":"https://www.academia.edu/Documents/in/Applied_Sciences"},{"id":3209333,"name":"Mean Squared Error","url":"https://www.academia.edu/Documents/in/Mean_Squared_Error"}],"urls":[{"id":40576780,"url":"https://www.mdpi.com/2076-3417/12/17/8671/pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116639349"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116639349/Optimization_and_prediction_of_tribological_behaviour_of_filled_polytetrafluoroethylene_composites_using_Taguchi_Deng_and_hybrid_support_vector_regression_models"><img alt="Research paper thumbnail of Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models" class="work-thumbnail" src="https://attachments.academia-assets.com/112712720/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116639349/Optimization_and_prediction_of_tribological_behaviour_of_filled_polytetrafluoroethylene_composites_using_Taguchi_Deng_and_hybrid_support_vector_regression_models">Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models</a></div><div class="wp-workCard_item"><span>Scientific Reports</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study presents optimization and prediction of tribological behaviour of filled polytetrafluo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses.To predict µ and Ks, SVR was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98903022"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98903022/Assessment_of_climate_change_impact_on_probable_maximum_floods_in_a_tropical_catchment"><img alt="Research paper thumbnail of Assessment of climate change impact on probable maximum floods in a tropical catchment" class="work-thumbnail" src="https://attachments.academia-assets.com/100130703/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98903022/Assessment_of_climate_change_impact_on_probable_maximum_floods_in_a_tropical_catchment">Assessment of climate change impact on probable maximum floods in a tropical catchment</a></div><div class="wp-workCard_item"><span>Theoretical and Applied Climatology</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a1f2f55a87d388be0089f5171b70381e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100130703,"asset_id":98903022,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100130703/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98903022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98903022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98903022; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98903022]").text(description); $(".js-view-count[data-work-id=98903022]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98903022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98903022']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 98903022, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a1f2f55a87d388be0089f5171b70381e" } } $('.js-work-strip[data-work-id=98903022]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98903022,"title":"Assessment of climate change impact on probable maximum floods in a tropical catchment","translated_title":"","metadata":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"Increased extreme rainfall due to climate change will increase the probable maximum ood (PMF) and pose a severe threat the critical hydraulic infrastructure like hydroelectric and ood protection dams. As the rainfall extremes in tropical regions are highly sensitive to global warming, increase PMF can be much higher in the tropics. A study has been conducted to assess the impact of climate change on PMF in a tropical catchment located in peninsular Malaysia. A lumped hydrological model, Mike NAM, is calibrated and validated with observed climate and in ow data of Tenmengor reservoir, located in the state of Perak of Peninsular Malaysia. Regional climate model projected rainfall is used to generate probable maximum precipitation (PMP) for future periods. The hydrological model is used to simulate PMF from PMP estimated for the historical and two future periods, early (2031−2045) and late (2060−2075). The results revealed the NAM model could simulate the river ow with a Nash-Sutcliffe e ciency of 0.74 and root mean square error of 0.51. The application of the model with projected rainfall revealed an increase in PMP by 162 to 507% and 259 to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating oods in the study area due to climate change.","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Theoretical and Applied Climatology","grobid_abstract_attachment_id":100130703},"translated_abstract":null,"internal_url":"https://www.academia.edu/98903022/Assessment_of_climate_change_impact_on_probable_maximum_floods_in_a_tropical_catchment","translated_internal_url":"","created_at":"2023-03-21T09:00:40.994-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":25846730,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":100130703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100130703/thumbnails/1.jpg","file_name":"latest.pdf","download_url":"https://www.academia.edu/attachments/100130703/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessment_of_climate_change_impact_on_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100130703/latest-libre.pdf?1679415405=\u0026response-content-disposition=attachment%3B+filename%3DAssessment_of_climate_change_impact_on_p.pdf\u0026Expires=1732833493\u0026Signature=Mh6gGuUhvV36wgFTQ7nZvputX6Ck1p5dGvqRMgMZGYWgGM19~0CWMTTeDPZd3DejqnCjcFNythDvMUfkx3fabgKfQgimE4Q5~6COT89nVoLDhcBqL4si1mYznAHxomkMl5UiOWw4xZ5FszK99513S3muci8RySV41sNqIvhalfkgLIA-LUeuBiYAG8Qij4quIvCaHPE3bvStUMXkbIAqLf04ZhnAa7VgbBR2UqLFgbTd49YmezsxmoxZzM8Ptdtcr3O~pl7V4s560XVmJqBCh7qF2cdj2ETnFib7yguk6dl9tBaPsiTpgt3fUhUCEdIId8Wd7n9wVWsU9KIsq5Le5Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessment_of_climate_change_impact_on_probable_maximum_floods_in_a_tropical_catchment","translated_slug":"","page_count":23,"language":"en","content_type":"Work","owner":{"id":25846730,"first_name":"Sani","middle_initials":null,"last_name":"Isah Abba","page_name":"SaniIsahAbba","domain_name":"neu-tr","created_at":"2015-02-05T19:58:48.343-08:00","display_name":"Sani Isah Abba","url":"https://neu-tr.academia.edu/SaniIsahAbba"},"attachments":[{"id":100130703,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100130703/thumbnails/1.jpg","file_name":"latest.pdf","download_url":"https://www.academia.edu/attachments/100130703/download_file?st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&st=MTczMjgyOTg5Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessment_of_climate_change_impact_on_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100130703/latest-libre.pdf?1679415405=\u0026response-content-disposition=attachment%3B+filename%3DAssessment_of_climate_change_impact_on_p.pdf\u0026Expires=1732833493\u0026Signature=Mh6gGuUhvV36wgFTQ7nZvputX6Ck1p5dGvqRMgMZGYWgGM19~0CWMTTeDPZd3DejqnCjcFNythDvMUfkx3fabgKfQgimE4Q5~6COT89nVoLDhcBqL4si1mYznAHxomkMl5UiOWw4xZ5FszK99513S3muci8RySV41sNqIvhalfkgLIA-LUeuBiYAG8Qij4quIvCaHPE3bvStUMXkbIAqLf04ZhnAa7VgbBR2UqLFgbTd49YmezsxmoxZzM8Ptdtcr3O~pl7V4s560XVmJqBCh7qF2cdj2ETnFib7yguk6dl9tBaPsiTpgt3fUhUCEdIId8Wd7n9wVWsU9KIsq5Le5Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1512,"name":"Climate Change","url":"https://www.academia.edu/Documents/in/Climate_Change"},{"id":3754,"name":"Climatology","url":"https://www.academia.edu/Documents/in/Climatology"},{"id":60285,"name":"Atmospheric sciences","url":"https://www.academia.edu/Documents/in/Atmospheric_sciences"},{"id":291658,"name":"Precipitation","url":"https://www.academia.edu/Documents/in/Precipitation"},{"id":2007066,"name":"Return period","url":"https://www.academia.edu/Documents/in/Return_period"},{"id":3647976,"name":"Flood Myth","url":"https://www.academia.edu/Documents/in/Flood_Myth"},{"id":4134089,"name":"Drainage basin","url":"https://www.academia.edu/Documents/in/Drainage_basin"}],"urls":[{"id":29975120,"url":"https://link.springer.com/content/pdf/10.1007/s00704-022-03925-9.pdf"}]}, dispatcherData: dispatcherData }); 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