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id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Dr.S.Evany Nithya</h3></div><div class="js-work-strip profile--work_container" data-work-id="106200152"><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/106200152/Biodegradation_capability_of_bacterial_species_isolated_from_oil_contaminated_soil"><img alt="Research paper thumbnail of Biodegradation capability of bacterial species isolated from oil contaminated soil" class="work-thumbnail" src="https://attachments.academia-assets.com/105458733/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/106200152/Biodegradation_capability_of_bacterial_species_isolated_from_oil_contaminated_soil">Biodegradation capability of bacterial species isolated from oil contaminated soil</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Oil spills do so much more damage than just the initial havoc we see on the news and the effects ...</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">Oil spills do so much more damage than just the initial havoc we see on the news and the effects can be very long lasting. Microorganisms are used to change harmful substances to non-toxic substances. This is a new technology for treating chemical spills and hazardous waste. This paper has studied the performance of different bacterial species in degrading the oil components. The degradation capability of isolated bacterial species were identified with solvent extraction method and confirmed by FT-IR analysis. Pseudomonas sp. found to degrade oil better than other isolated species. Pseudomonas degraded 90.2% of oil in 30 d followed by 82.3% of oil degraded by Bacillus, 78.8% of oil degraded by Serratia and 25.5% of oil degraded by Staphylococcus. Then, Pseudomonas was immobilized using sodium alginate method and no decline in biodegradation capacity of the immobilized consortium of bacterial cells was noted. The mixed bacterial culture degraded 97.2% of the oil components during 30 ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="371cac262c6f64814e31f35ca7b2d5cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105458733,"asset_id":106200152,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105458733/download_file?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="106200152"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200152"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200152; 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$(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="106200151"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200151/An_Integrated_Approach_with_GIS_and_Remote_Sensing_Technique_for_Landslide_Hazard_Zonation"><img alt="Research paper thumbnail of An Integrated Approach with GIS and Remote Sensing Technique for Landslide Hazard Zonation" 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" rel="nofollow" href="https://www.academia.edu/106200151/An_Integrated_Approach_with_GIS_and_Remote_Sensing_Technique_for_Landslide_Hazard_Zonation">An Integrated Approach with GIS and Remote Sensing Technique for Landslide Hazard Zonation</a></div><div class="wp-workCard_item"><span>International Journal of Geomatics and Geosciences</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslide is among the major hydro颅geological hazards that affect large parts of India, especiall...</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">Landslide is among the major hydro颅geological hazards that affect large parts of India, especially Himalayas, North颅 Eastern hill ranges, Western Ghats, Nilgiris and Eastern Ghats. Landslide hazard zonation helps in identifying strategic points and geographically critical areas prone to landslides. This paper deals with an integrated approach of remote sensing and geographical information systems for landslide hazard zonation. Landslide hazard zonation simply means the division and preferably subdivision of land surface in to various zones according to the degrees of actual potential hazard caused by landslides and related phenomena. The key factor in landslide hazard mapping is the assessment and grading of risk associated with the failures. The landslide distributions are mapped by extraction of features from the satellite images. In order to analyze landslide susceptibility efficiently, the existing maps are digitized and processed with the help of integration of terrain information by GIS. Six categories of controlling factors for landslides i.e. aspect of slope, geology, land use, drainage, lineament and runoff are defined. The data obtained is integrated to form landslide hazard zonation map. An effort is made to generate a landslide susceptible map from satellite imagery, DEM (Digital Elevation Model) and maps. Finally, a map divided into four susceptibility zones is produced using the weight value of all controlling factors using the method of multi objective decision making process and then each susceptibility zone is evaluated by comparing with the distribution of each controlling factor class. Landslide susceptible map can be used to predict the occurrence of the landslide and also used for planning developmental activities in this geomorphologically fragile Nilgiris plateau.</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="106200151"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200151"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200151; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200151]").text(description); $(".js-view-count[data-work-id=106200151]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200151; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200151']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=106200151]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106200151,"title":"An Integrated Approach with GIS and Remote Sensing Technique for Landslide Hazard Zonation","internal_url":"https://www.academia.edu/106200151/An_Integrated_Approach_with_GIS_and_Remote_Sensing_Technique_for_Landslide_Hazard_Zonation","owner_id":274778279,"coauthors_can_edit":true,"owner":{"id":274778279,"first_name":"Dr.S.Evany","middle_initials":null,"last_name":"Nithya","page_name":"DrSEvanyNithya","domain_name":"independent","created_at":"2023-06-23T21:46:23.125-07:00","display_name":"Dr.S.Evany Nithya","url":"https://independent.academia.edu/DrSEvanyNithya"},"attachments":[]}, 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="106200150"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200150/Landslide_Suceptibility_Zonation_using_Fuzzy_Logic_for_Kundahpallam_Watershed_Nilgris"><img alt="Research paper thumbnail of Landslide Suceptibility Zonation using Fuzzy Logic for Kundahpallam Watershed, Nilgris" 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" rel="nofollow" href="https://www.academia.edu/106200150/Landslide_Suceptibility_Zonation_using_Fuzzy_Logic_for_Kundahpallam_Watershed_Nilgris">Landslide Suceptibility Zonation using Fuzzy Logic for Kundahpallam Watershed, Nilgris</a></div><div class="wp-workCard_item"><span>European Journal of Scientific Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslide hazard zonation map has been prepared for the Kundahpallam watershed using fuzzy logic....</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">Landslide hazard zonation map has been prepared for the Kundahpallam watershed using fuzzy logic. This watershed is located within Nilgiris district of Tamil Nadu state and occupies an area of 4,839 hectares. Several landslides were reported earlier in this area,and there is a growing concern over the loss of lives and economy. Hence, an assessment of landslide susceptible areas became essential. In this work, such an assessment is made employing trapezoidal fuzzy logic membership functions where weight of each thematic layer is calculated from the fuzzy numbers generated by the membership function. Finally, the instability score is calculated using the weights. The results obtained from this study shows that around 48% of the study area falls under very high and highly susceptibility category 33% of the area is moderately susceptible, and 18% of the area falls under low susceptibility category. Nearly 82% of the tea plantation area was found to be vulnerable to landslide. These res...</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="106200150"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200150"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200150; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200150]").text(description); $(".js-view-count[data-work-id=106200150]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200150; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200150']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=106200150]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106200150,"title":"Landslide Suceptibility Zonation using Fuzzy Logic for Kundahpallam Watershed, Nilgris","internal_url":"https://www.academia.edu/106200150/Landslide_Suceptibility_Zonation_using_Fuzzy_Logic_for_Kundahpallam_Watershed_Nilgris","owner_id":274778279,"coauthors_can_edit":true,"owner":{"id":274778279,"first_name":"Dr.S.Evany","middle_initials":null,"last_name":"Nithya","page_name":"DrSEvanyNithya","domain_name":"independent","created_at":"2023-06-23T21:46:23.125-07:00","display_name":"Dr.S.Evany Nithya","url":"https://independent.academia.edu/DrSEvanyNithya"},"attachments":[]}, 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="106200149"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200149/Landslide_Prediction_Model_for_Kundhapalam_Watershed_Using_Fuzzy_Algebraic_Operators"><img alt="Research paper thumbnail of Landslide Prediction Model for Kundhapalam Watershed Using Fuzzy Algebraic Operators" 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" rel="nofollow" href="https://www.academia.edu/106200149/Landslide_Prediction_Model_for_Kundhapalam_Watershed_Using_Fuzzy_Algebraic_Operators">Landslide Prediction Model for Kundhapalam Watershed Using Fuzzy Algebraic Operators</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslide is one of the major natural geological hazards, which is responsible for enormous prope...</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">Landslide is one of the major natural geological hazards, which is responsible for enormous property damage and both direct and indirect costs. This paper presents an exploratory attempt on the use of Fuzzy Logic rules for Landslide Susceptibility Assessment in complex mountainous environments. Using the factors influencing the landslide and the landslide locations, the fuzzy membership values were calculated. Then the fuzzy operators and algebraic operators were used to map the landslide susceptibility. Among the operators the algebraic operator showed the best accuracy when compared with the landslide inventory map. Fuzzy SUM and OR showed the worst results.</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="106200149"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200149; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200149]").text(description); $(".js-view-count[data-work-id=106200149]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200149; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200149']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=106200149]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106200149,"title":"Landslide Prediction Model for Kundhapalam Watershed Using Fuzzy Algebraic Operators","internal_url":"https://www.academia.edu/106200149/Landslide_Prediction_Model_for_Kundhapalam_Watershed_Using_Fuzzy_Algebraic_Operators","owner_id":274778279,"coauthors_can_edit":true,"owner":{"id":274778279,"first_name":"Dr.S.Evany","middle_initials":null,"last_name":"Nithya","page_name":"DrSEvanyNithya","domain_name":"independent","created_at":"2023-06-23T21:46:23.125-07:00","display_name":"Dr.S.Evany Nithya","url":"https://independent.academia.edu/DrSEvanyNithya"},"attachments":[]}, 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="106200148"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200148/Comparison_of_Fuzzy_Model_and_Frequency_Ratio_Model_for_the_analysis_of_Kundha_Landslides_in_Nilgiris_District"><img alt="Research paper thumbnail of Comparison of Fuzzy Model and Frequency Ratio Model for the analysis of Kundha Landslides in Nilgiris District" 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" rel="nofollow" href="https://www.academia.edu/106200148/Comparison_of_Fuzzy_Model_and_Frequency_Ratio_Model_for_the_analysis_of_Kundha_Landslides_in_Nilgiris_District">Comparison of Fuzzy Model and Frequency Ratio Model for the analysis of Kundha Landslides in Nilgiris District</a></div><div class="wp-workCard_item"><span>Disaster Advances</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslides pose a major threat in mountainous regions like Nilgiris. Although most of the landsli...</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">Landslides pose a major threat in mountainous regions like Nilgiris. Although most of the landslides occur during rainy season, the geographic location of such landslides remains unpredictable, posing significant disaster management problems. Although a number of methods are available, fuzzy-logic provides an easy and intuitive way for assessment of landslide susceptibility. However, the validity of fuzzy-logic models with statistical methods viz., frequency-ratio method complemented with field observation shall provide a robust basis for application of these models for specific and adjoining geographical areas. Such a kind of comparison was made using the susceptibility index map developed by considering the causative factors such as slope, soil depth, geomorphology, geology, land use, drainage density, lineament density, runoff and proximity to the road. Using linear membership function, the susceptibility indices developed by both methods were rescaled between 0 and 1 for compari...</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="106200148"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200148"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200148; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200148]").text(description); 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</script> <div class="js-work-strip profile--work_container" data-work-id="106200142"><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/106200142/Estimation_of_LANDSAT_model_and_Normalized_Difference_Vegetation_Index_values_for_Namakkal_District_using_Remote_sensing_and_GIS"><img alt="Research paper thumbnail of Estimation of LANDSAT model and Normalized Difference Vegetation Index values for Namakkal District using Remote sensing and GIS" class="work-thumbnail" src="https://attachments.academia-assets.com/105458745/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/106200142/Estimation_of_LANDSAT_model_and_Normalized_Difference_Vegetation_Index_values_for_Namakkal_District_using_Remote_sensing_and_GIS">Estimation of LANDSAT model and Normalized Difference Vegetation Index values for Namakkal District using Remote sensing and GIS</a></div><div class="wp-workCard_item"><span>International journal of engineering research and technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Over the past few decades many investigations has been carried in order to audit the changes and ...</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">Over the past few decades many investigations has been carried in order to audit the changes and the variations in the land surface temperature research has been carried. The climate change and global warming is one of the greatest concern about the LST. In this study an attempt has been made to estimate the surface temperatures of Namakkal district in Tamil Nadu, India using remote sensing datasets observed by Landsat 5(Thematic Mapper), Landsat 7 (Enhanced Thematic Mapper+) and Landsat 8 (Thermal Infra-Red Sensors) satellites. NVDI values are determined from the Landsat Near Infra-Red and Infra-Red channels that has been redeemed from these above satellite datasets. Over the decades it has been clear that LST has been increased. To comprehend the local temperature change the medley of Land Surface Temperature and Normalized Difference Vegetation Index values gives clear data about the vegetation of the study area and areas can be classified into a green sustainable and eco-friendly areas.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7ca3a7ff882de27d4f256be2083e3efb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105458745,"asset_id":106200142,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105458745/download_file?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="106200142"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200142"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200142; 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$(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="18505586" id="papers"><div class="js-work-strip profile--work_container" data-work-id="106200152"><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/106200152/Biodegradation_capability_of_bacterial_species_isolated_from_oil_contaminated_soil"><img alt="Research paper thumbnail of Biodegradation capability of bacterial species isolated from oil contaminated soil" class="work-thumbnail" src="https://attachments.academia-assets.com/105458733/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/106200152/Biodegradation_capability_of_bacterial_species_isolated_from_oil_contaminated_soil">Biodegradation capability of bacterial species isolated from oil contaminated soil</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Oil spills do so much more damage than just the initial havoc we see on the news and the effects ...</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">Oil spills do so much more damage than just the initial havoc we see on the news and the effects can be very long lasting. Microorganisms are used to change harmful substances to non-toxic substances. This is a new technology for treating chemical spills and hazardous waste. This paper has studied the performance of different bacterial species in degrading the oil components. The degradation capability of isolated bacterial species were identified with solvent extraction method and confirmed by FT-IR analysis. Pseudomonas sp. found to degrade oil better than other isolated species. Pseudomonas degraded 90.2% of oil in 30 d followed by 82.3% of oil degraded by Bacillus, 78.8% of oil degraded by Serratia and 25.5% of oil degraded by Staphylococcus. Then, Pseudomonas was immobilized using sodium alginate method and no decline in biodegradation capacity of the immobilized consortium of bacterial cells was noted. The mixed bacterial culture degraded 97.2% of the oil components during 30 ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="371cac262c6f64814e31f35ca7b2d5cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105458733,"asset_id":106200152,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105458733/download_file?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="106200152"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200152"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200152; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200152]").text(description); $(".js-view-count[data-work-id=106200152]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200152; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200152']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="106200151"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200151/An_Integrated_Approach_with_GIS_and_Remote_Sensing_Technique_for_Landslide_Hazard_Zonation"><img alt="Research paper thumbnail of An Integrated Approach with GIS and Remote Sensing Technique for Landslide Hazard Zonation" 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" rel="nofollow" href="https://www.academia.edu/106200151/An_Integrated_Approach_with_GIS_and_Remote_Sensing_Technique_for_Landslide_Hazard_Zonation">An Integrated Approach with GIS and Remote Sensing Technique for Landslide Hazard Zonation</a></div><div class="wp-workCard_item"><span>International Journal of Geomatics and Geosciences</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslide is among the major hydro颅geological hazards that affect large parts of India, especiall...</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">Landslide is among the major hydro颅geological hazards that affect large parts of India, especially Himalayas, North颅 Eastern hill ranges, Western Ghats, Nilgiris and Eastern Ghats. Landslide hazard zonation helps in identifying strategic points and geographically critical areas prone to landslides. This paper deals with an integrated approach of remote sensing and geographical information systems for landslide hazard zonation. Landslide hazard zonation simply means the division and preferably subdivision of land surface in to various zones according to the degrees of actual potential hazard caused by landslides and related phenomena. The key factor in landslide hazard mapping is the assessment and grading of risk associated with the failures. The landslide distributions are mapped by extraction of features from the satellite images. In order to analyze landslide susceptibility efficiently, the existing maps are digitized and processed with the help of integration of terrain information by GIS. Six categories of controlling factors for landslides i.e. aspect of slope, geology, land use, drainage, lineament and runoff are defined. The data obtained is integrated to form landslide hazard zonation map. An effort is made to generate a landslide susceptible map from satellite imagery, DEM (Digital Elevation Model) and maps. Finally, a map divided into four susceptibility zones is produced using the weight value of all controlling factors using the method of multi objective decision making process and then each susceptibility zone is evaluated by comparing with the distribution of each controlling factor class. Landslide susceptible map can be used to predict the occurrence of the landslide and also used for planning developmental activities in this geomorphologically fragile Nilgiris plateau.</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="106200151"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200151"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200151; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200151]").text(description); $(".js-view-count[data-work-id=106200151]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200151; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200151']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=106200151]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106200151,"title":"An Integrated Approach with GIS and Remote Sensing Technique for Landslide Hazard Zonation","internal_url":"https://www.academia.edu/106200151/An_Integrated_Approach_with_GIS_and_Remote_Sensing_Technique_for_Landslide_Hazard_Zonation","owner_id":274778279,"coauthors_can_edit":true,"owner":{"id":274778279,"first_name":"Dr.S.Evany","middle_initials":null,"last_name":"Nithya","page_name":"DrSEvanyNithya","domain_name":"independent","created_at":"2023-06-23T21:46:23.125-07:00","display_name":"Dr.S.Evany Nithya","url":"https://independent.academia.edu/DrSEvanyNithya"},"attachments":[]}, 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="106200150"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200150/Landslide_Suceptibility_Zonation_using_Fuzzy_Logic_for_Kundahpallam_Watershed_Nilgris"><img alt="Research paper thumbnail of Landslide Suceptibility Zonation using Fuzzy Logic for Kundahpallam Watershed, Nilgris" 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" rel="nofollow" href="https://www.academia.edu/106200150/Landslide_Suceptibility_Zonation_using_Fuzzy_Logic_for_Kundahpallam_Watershed_Nilgris">Landslide Suceptibility Zonation using Fuzzy Logic for Kundahpallam Watershed, Nilgris</a></div><div class="wp-workCard_item"><span>European Journal of Scientific Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslide hazard zonation map has been prepared for the Kundahpallam watershed using fuzzy logic....</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">Landslide hazard zonation map has been prepared for the Kundahpallam watershed using fuzzy logic. This watershed is located within Nilgiris district of Tamil Nadu state and occupies an area of 4,839 hectares. Several landslides were reported earlier in this area,and there is a growing concern over the loss of lives and economy. Hence, an assessment of landslide susceptible areas became essential. In this work, such an assessment is made employing trapezoidal fuzzy logic membership functions where weight of each thematic layer is calculated from the fuzzy numbers generated by the membership function. Finally, the instability score is calculated using the weights. The results obtained from this study shows that around 48% of the study area falls under very high and highly susceptibility category 33% of the area is moderately susceptible, and 18% of the area falls under low susceptibility category. Nearly 82% of the tea plantation area was found to be vulnerable to landslide. These res...</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="106200150"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200150"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200150; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200150]").text(description); $(".js-view-count[data-work-id=106200150]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200150; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200150']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=106200150]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106200150,"title":"Landslide Suceptibility Zonation using Fuzzy Logic for Kundahpallam Watershed, Nilgris","internal_url":"https://www.academia.edu/106200150/Landslide_Suceptibility_Zonation_using_Fuzzy_Logic_for_Kundahpallam_Watershed_Nilgris","owner_id":274778279,"coauthors_can_edit":true,"owner":{"id":274778279,"first_name":"Dr.S.Evany","middle_initials":null,"last_name":"Nithya","page_name":"DrSEvanyNithya","domain_name":"independent","created_at":"2023-06-23T21:46:23.125-07:00","display_name":"Dr.S.Evany Nithya","url":"https://independent.academia.edu/DrSEvanyNithya"},"attachments":[]}, 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="106200149"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200149/Landslide_Prediction_Model_for_Kundhapalam_Watershed_Using_Fuzzy_Algebraic_Operators"><img alt="Research paper thumbnail of Landslide Prediction Model for Kundhapalam Watershed Using Fuzzy Algebraic Operators" 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" rel="nofollow" href="https://www.academia.edu/106200149/Landslide_Prediction_Model_for_Kundhapalam_Watershed_Using_Fuzzy_Algebraic_Operators">Landslide Prediction Model for Kundhapalam Watershed Using Fuzzy Algebraic Operators</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslide is one of the major natural geological hazards, which is responsible for enormous prope...</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">Landslide is one of the major natural geological hazards, which is responsible for enormous property damage and both direct and indirect costs. This paper presents an exploratory attempt on the use of Fuzzy Logic rules for Landslide Susceptibility Assessment in complex mountainous environments. Using the factors influencing the landslide and the landslide locations, the fuzzy membership values were calculated. Then the fuzzy operators and algebraic operators were used to map the landslide susceptibility. Among the operators the algebraic operator showed the best accuracy when compared with the landslide inventory map. Fuzzy SUM and OR showed the worst results.</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="106200149"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200149; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200149]").text(description); $(".js-view-count[data-work-id=106200149]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106200149; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106200149']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=106200149]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106200149,"title":"Landslide Prediction Model for Kundhapalam Watershed Using Fuzzy Algebraic Operators","internal_url":"https://www.academia.edu/106200149/Landslide_Prediction_Model_for_Kundhapalam_Watershed_Using_Fuzzy_Algebraic_Operators","owner_id":274778279,"coauthors_can_edit":true,"owner":{"id":274778279,"first_name":"Dr.S.Evany","middle_initials":null,"last_name":"Nithya","page_name":"DrSEvanyNithya","domain_name":"independent","created_at":"2023-06-23T21:46:23.125-07:00","display_name":"Dr.S.Evany Nithya","url":"https://independent.academia.edu/DrSEvanyNithya"},"attachments":[]}, 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="106200148"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/106200148/Comparison_of_Fuzzy_Model_and_Frequency_Ratio_Model_for_the_analysis_of_Kundha_Landslides_in_Nilgiris_District"><img alt="Research paper thumbnail of Comparison of Fuzzy Model and Frequency Ratio Model for the analysis of Kundha Landslides in Nilgiris District" 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" rel="nofollow" href="https://www.academia.edu/106200148/Comparison_of_Fuzzy_Model_and_Frequency_Ratio_Model_for_the_analysis_of_Kundha_Landslides_in_Nilgiris_District">Comparison of Fuzzy Model and Frequency Ratio Model for the analysis of Kundha Landslides in Nilgiris District</a></div><div class="wp-workCard_item"><span>Disaster Advances</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Landslides pose a major threat in mountainous regions like Nilgiris. Although most of the landsli...</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">Landslides pose a major threat in mountainous regions like Nilgiris. Although most of the landslides occur during rainy season, the geographic location of such landslides remains unpredictable, posing significant disaster management problems. Although a number of methods are available, fuzzy-logic provides an easy and intuitive way for assessment of landslide susceptibility. However, the validity of fuzzy-logic models with statistical methods viz., frequency-ratio method complemented with field observation shall provide a robust basis for application of these models for specific and adjoining geographical areas. Such a kind of comparison was made using the susceptibility index map developed by considering the causative factors such as slope, soil depth, geomorphology, geology, land use, drainage density, lineament density, runoff and proximity to the road. Using linear membership function, the susceptibility indices developed by both methods were rescaled between 0 and 1 for compari...</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="106200148"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200148"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200148; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106200148]").text(description); 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</script> <div class="js-work-strip profile--work_container" data-work-id="106200142"><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/106200142/Estimation_of_LANDSAT_model_and_Normalized_Difference_Vegetation_Index_values_for_Namakkal_District_using_Remote_sensing_and_GIS"><img alt="Research paper thumbnail of Estimation of LANDSAT model and Normalized Difference Vegetation Index values for Namakkal District using Remote sensing and GIS" class="work-thumbnail" src="https://attachments.academia-assets.com/105458745/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/106200142/Estimation_of_LANDSAT_model_and_Normalized_Difference_Vegetation_Index_values_for_Namakkal_District_using_Remote_sensing_and_GIS">Estimation of LANDSAT model and Normalized Difference Vegetation Index values for Namakkal District using Remote sensing and GIS</a></div><div class="wp-workCard_item"><span>International journal of engineering research and technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Over the past few decades many investigations has been carried in order to audit the changes and ...</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">Over the past few decades many investigations has been carried in order to audit the changes and the variations in the land surface temperature research has been carried. The climate change and global warming is one of the greatest concern about the LST. In this study an attempt has been made to estimate the surface temperatures of Namakkal district in Tamil Nadu, India using remote sensing datasets observed by Landsat 5(Thematic Mapper), Landsat 7 (Enhanced Thematic Mapper+) and Landsat 8 (Thermal Infra-Red Sensors) satellites. NVDI values are determined from the Landsat Near Infra-Red and Infra-Red channels that has been redeemed from these above satellite datasets. Over the decades it has been clear that LST has been increased. To comprehend the local temperature change the medley of Land Surface Temperature and Normalized Difference Vegetation Index values gives clear data about the vegetation of the study area and areas can be classified into a green sustainable and eco-friendly areas.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7ca3a7ff882de27d4f256be2083e3efb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105458745,"asset_id":106200142,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105458745/download_file?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="106200142"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106200142"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106200142; 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