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prediction</title> <meta name="description" content="Search results for: weather prediction"> <meta name="keywords" content="weather prediction"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="weather prediction"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2966</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: weather prediction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2966</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2965</span> Application of Bayesian Model Averaging and Geostatistical Output Perturbation to Generate Calibrated Ensemble Weather Forecast</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Luthfi">Muhammad Luthfi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sutikno%20Sutikno"> Sutikno Sutikno</a>, <a href="https://publications.waset.org/abstracts/search?q=Purhadi%20Purhadi"> Purhadi Purhadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weather forecast has necessarily been improved to provide the communities an accurate and objective prediction as well. To overcome such issue, the numerical-based weather forecast was extensively developed to reduce the subjectivity of forecast. Yet the Numerical Weather Predictions (NWPs) outputs are unfortunately issued without taking dynamical weather behavior and local terrain features into account. Thus, NWPs outputs are not able to accurately forecast the weather quantities, particularly for medium and long range forecast. The aim of this research is to aid and extend the development of ensemble forecast for Meteorology, Climatology, and Geophysics Agency of Indonesia. Ensemble method is an approach combining various deterministic forecast to produce more reliable one. However, such forecast is biased and uncalibrated due to its underdispersive or overdispersive nature. As one of the parametric methods, Bayesian Model Averaging (BMA) generates the calibrated ensemble forecast and constructs predictive PDF for specified period. Such method is able to utilize ensemble of any size but does not take spatial correlation into account. Whereas space dependencies involve the site of interest and nearby site, influenced by dynamic weather behavior. Meanwhile, Geostatistical Output Perturbation (GOP) reckons the spatial correlation to generate future weather quantities, though merely built by a single deterministic forecast, and is able to generate an ensemble of any size as well. This research conducts both BMA and GOP to generate the calibrated ensemble forecast for the daily temperature at few meteorological sites nearby Indonesia international airport. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20Model%20Averaging" title="Bayesian Model Averaging">Bayesian Model Averaging</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20forecast" title=" ensemble forecast"> ensemble forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistical%20output%20perturbation" title=" geostatistical output perturbation"> geostatistical output perturbation</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20weather%20prediction" title=" numerical weather prediction"> numerical weather prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a> </p> <a href="https://publications.waset.org/abstracts/68771/application-of-bayesian-model-averaging-and-geostatistical-output-perturbation-to-generate-calibrated-ensemble-weather-forecast" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2964</span> Mobile Based Long Range Weather Prediction System for the Farmers of Rural Areas of Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeeshan%20Muzammal">Zeeshan Muzammal</a>, <a href="https://publications.waset.org/abstracts/search?q=Usama%20Latif"> Usama Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=Fouzia%20Younas"> Fouzia Younas</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Muhammad%20Hassan"> Syed Muhammad Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Samia%20Razaq"> Samia Razaq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ICTD" title="ICTD">ICTD</a>, <a href="https://publications.waset.org/abstracts/search?q=farmers" title=" farmers"> farmers</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20based" title=" mobile based"> mobile based</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakistan" title=" Pakistan"> Pakistan</a>, <a href="https://publications.waset.org/abstracts/search?q=rural%20areas" title=" rural areas"> rural areas</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20prediction" title=" weather prediction "> weather prediction </a> </p> <a href="https://publications.waset.org/abstracts/60473/mobile-based-long-range-weather-prediction-system-for-the-farmers-of-rural-areas-of-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">572</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2963</span> A Deep Learning Approach to Real Time and Robust Vehicular Traffic Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bikis%20Muhammed">Bikis Muhammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sehra%20Sedigh%20Sarvestani"> Sehra Sedigh Sarvestani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20R.%20Hurson"> Ali R. Hurson</a>, <a href="https://publications.waset.org/abstracts/search?q=Lasanthi%20Gamage"> Lasanthi Gamage</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20prediction" title=" real time prediction"> real time prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=GAT" title=" GAT"> GAT</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title=" Bi-LSTM"> Bi-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a> </p> <a href="https://publications.waset.org/abstracts/170750/a-deep-learning-approach-to-real-time-and-robust-vehicular-traffic-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2962</span> A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Mugume">Isaac Mugume</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20Basalirwa"> Charles Basalirwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Waiswa"> Daniel Waiswa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mary%20Nsabagwa"> Mary Nsabagwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Triphonia%20Jacob%20Ngailo"> Triphonia Jacob Ngailo</a>, <a href="https://publications.waset.org/abstracts/search?q=Joachim%20Reuder"> Joachim Reuder</a>, <a href="https://publications.waset.org/abstracts/search?q=Sch%C2%A8attler%20Ulrich"> Sch¨attler Ulrich</a>, <a href="https://publications.waset.org/abstracts/search?q=Musa%20Semujju"> Musa Semujju</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative%20performance" title="comparative performance">comparative performance</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20COSMO%20model" title=" the COSMO model"> the COSMO model</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20WRF%20model" title=" the WRF model"> the WRF model</a>, <a href="https://publications.waset.org/abstracts/search?q=light%20rainfall%20events" title=" light rainfall events"> light rainfall events</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20rainfall%20events" title=" extreme rainfall events"> extreme rainfall events</a> </p> <a href="https://publications.waset.org/abstracts/88050/a-comparative-analysis-of-the-performance-of-cosmo-and-wrf-models-in-quantitative-rainfall-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88050.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2961</span> Statistical Scientific Investigation of Popular Cultural Heritage in the Relationship between Astronomy and Weather Conditions in the State of Kuwait</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20M.%20AlHasem">Ahmed M. AlHasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Kuwaiti society has long been aware of climatic changes and their annual dates and trying to link them to astronomy in an attempt to forecast the future weather conditions. The reason for this concern is that many of the economic, social and living activities of the society depend deeply on the nature of the weather conditions directly and indirectly. In other words, Kuwaiti society, like the case of many human societies, has in the past tried to predict climatic conditions by linking them to astronomy or popular statements to indicate the timing of climate changes. Accordingly, this study was devoted to scientific investigation based on the statistical analysis of climatic data to show the accuracy and compatibility of some of the most important elements of the cultural heritage in relation to climate change and to relate it scientifically to precise climatic measurements for decades. The research has been divided into 10 topics, each topic has been focused on one legacy, whether by linking climate changes to the appearance/disappearance of star or a popular statement inherited through generations, through explain the nature and timing and thereby statistical analysis to indicate the proportion of accuracy based on official climatic data since 1962. The study's conclusion is that the relationship is weak and, in some cases, non-existent between the popular heritage and the actual climatic data. Therefore, it does not have a dependable relationship and a reliable scientific prediction between both the popular heritage and the forecast of weather conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=astronomy" title="astronomy">astronomy</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20heritage" title=" cultural heritage"> cultural heritage</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20prediction" title=" weather prediction"> weather prediction</a> </p> <a href="https://publications.waset.org/abstracts/102495/statistical-scientific-investigation-of-popular-cultural-heritage-in-the-relationship-between-astronomy-and-weather-conditions-in-the-state-of-kuwait" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102495.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2960</span> Spatial Variation of WRF Model Rainfall Prediction over Uganda</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Mugume">Isaac Mugume</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20Basalirwa"> Charles Basalirwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Waiswa"> Daniel Waiswa</a>, <a href="https://publications.waset.org/abstracts/search?q=Triphonia%20Ngailo"> Triphonia Ngailo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convective%20parameterization%20schemes" title="convective parameterization schemes">convective parameterization schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=March-May%202013%20rainfall%20season" title=" March-May 2013 rainfall season"> March-May 2013 rainfall season</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20variation%20of%20parameterization%20schemes%20over%20Uganda" title=" spatial variation of parameterization schemes over Uganda"> spatial variation of parameterization schemes over Uganda</a>, <a href="https://publications.waset.org/abstracts/search?q=WRF%20model" title=" WRF model"> WRF model</a> </p> <a href="https://publications.waset.org/abstracts/69722/spatial-variation-of-wrf-model-rainfall-prediction-over-uganda" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2959</span> Improved Soil and Snow Treatment with the Rapid Update Cycle Land-Surface Model for Regional and Global Weather Predictions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20G.%20Smirnova">Tatiana G. Smirnova</a>, <a href="https://publications.waset.org/abstracts/search?q=Stan%20G.%20Benjamin"> Stan G. Benjamin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rapid Update Cycle (RUC) land surface model (LSM) was a land-surface component in several generations of operational weather prediction models at the National Center for Environment Prediction (NCEP) at the National Oceanic and Atmospheric Administration (NOAA). It was designed for short-range weather predictions with an emphasis on severe weather and originally was intentionally simple to avoid uncertainties from poorly known parameters. Nevertheless, the RUC LSM, when coupled with the hourly-assimilating atmospheric model, can produce a realistic evolution of time-varying soil moisture and temperature, as well as the evolution of snow cover on the ground surface. This result is possible only if the soil/vegetation/snow component of the coupled weather prediction model has sufficient skill to avoid long-term drift. RUC LSM was first implemented in the operational NCEP Rapid Update Cycle (RUC) weather model in 1998 and later in the Weather Research Forecasting Model (WRF)-based Rapid Refresh (RAP) and High-resolution Rapid Refresh (HRRR). Being available to the international WRF community, it was implemented in operational weather models in Austria, New Zealand, and Switzerland. Based on the feedback from the US weather service offices and the international WRF community and also based on our own validation, RUC LSM has matured over the years. Also, a sea-ice module was added to RUC LSM for surface predictions over the Arctic sea-ice. Other modifications include refinements to the snow model and a more accurate specification of albedo, roughness length, and other surface properties. At present, RUC LSM is being tested in the regional application of the Unified Forecast System (UFS). The next generation UFS-based regional Rapid Refresh FV3 Standalone (RRFS) model will replace operational RAP and HRRR at NCEP. Over time, RUC LSM participated in several international model intercomparison projects to verify its skill using observed atmospheric forcing. The ESM-SnowMIP was the last of these experiments focused on the verification of snow models for open and forested regions. The simulations were performed for ten sites located in different climatic zones of the world forced with observed atmospheric conditions. While most of the 26 participating models have more sophisticated snow parameterizations than in RUC, RUC LSM got a high ranking in simulations of both snow water equivalent and surface temperature. However, ESM-SnowMIP experiment also revealed some issues in the RUC snow model, which will be addressed in this paper. One of them is the treatment of grid cells partially covered with snow. RUC snow module computes energy and moisture budgets of snow-covered and snow-free areas separately by aggregating the solutions at the end of each time step. Such treatment elevates the importance of computing in the model snow cover fraction. Improvements to the original simplistic threshold-based approach have been implemented and tested both offline and in the coupled weather model. The detailed description of changes to the snow cover fraction and other modifications to RUC soil and snow parameterizations will be described in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land-surface%20models" title="land-surface models">land-surface models</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20prediction" title=" weather prediction"> weather prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrology" title=" hydrology"> hydrology</a>, <a href="https://publications.waset.org/abstracts/search?q=boundary-layer%20processes" title=" boundary-layer processes"> boundary-layer processes</a> </p> <a href="https://publications.waset.org/abstracts/166925/improved-soil-and-snow-treatment-with-the-rapid-update-cycle-land-surface-model-for-regional-and-global-weather-predictions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2958</span> Representation Data without Lost Compression Properties in Time Series: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nabilah%20Filzah%20Mohd%20Radzuan">Nabilah Filzah Mohd Radzuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zalinda%20Othman"> Zalinda Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Azuraliza%20Abu%20Bakar"> Azuraliza Abu Bakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Razak%20Hamdan"> Abdul Razak Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20properties" title="compression properties">compression properties</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20time%20series" title=" uncertain time series"> uncertain time series</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20technique" title=" mining technique"> mining technique</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20prediction" title=" weather prediction"> weather prediction</a> </p> <a href="https://publications.waset.org/abstracts/1419/representation-data-without-lost-compression-properties-in-time-series-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1419.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2957</span> Development of a Wind Resource Assessment Framework Using Weather Research and Forecasting (WRF) Model, Python Scripting and Geographic Information Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jerome%20T.%20Tolentino">Jerome T. Tolentino</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Victoria%20Rejuso"> Ma. Victoria Rejuso</a>, <a href="https://publications.waset.org/abstracts/search?q=Jara%20Kaye%20Villanueva"> Jara Kaye Villanueva</a>, <a href="https://publications.waset.org/abstracts/search?q=Loureal%20Camille%20Inocencio"> Loureal Camille Inocencio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Rosario%20Concepcion%20O.%20Ang"> Ma. Rosario Concepcion O. Ang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wind energy is rapidly emerging as the primary source of electricity in the Philippines, although developing an accurate wind resource model is difficult. In this study, Weather Research and Forecasting (WRF) Model, an open source mesoscale Numerical Weather Prediction (NWP) model, was used to produce a 1-year atmospheric simulation with 4 km resolution on the Ilocos Region of the Philippines. The WRF output (netCDF) extracts the annual mean wind speed data using a Python-based Graphical User Interface. Lastly, wind resource assessment was produced using a GIS software. Results of the study showed that it is more flexible to use Python scripts than using other post-processing tools in dealing with netCDF files. Using WRF Model, Python, and Geographic Information Systems, a reliable wind resource map is produced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20resource%20assessment" title="wind resource assessment">wind resource assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20research%20and%20forecasting%20%28WRF%29%20model" title=" weather research and forecasting (WRF) model"> weather research and forecasting (WRF) model</a>, <a href="https://publications.waset.org/abstracts/search?q=python" title=" python"> python</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS%20software" title=" GIS software"> GIS software</a> </p> <a href="https://publications.waset.org/abstracts/40795/development-of-a-wind-resource-assessment-framework-using-weather-research-and-forecasting-wrf-model-python-scripting-and-geographic-information-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2956</span> Solar Radiation Time Series Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cameron%20Hamilton">Cameron Hamilton</a>, <a href="https://publications.waset.org/abstracts/search?q=Walter%20Potter"> Walter Potter</a>, <a href="https://publications.waset.org/abstracts/search?q=Gerrit%20Hoogenboom"> Gerrit Hoogenboom</a>, <a href="https://publications.waset.org/abstracts/search?q=Ronald%20McClendon"> Ronald McClendon</a>, <a href="https://publications.waset.org/abstracts/search?q=Will%20Hobbs"> Will Hobbs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=resilient%20propagation" title=" resilient propagation"> resilient propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20radiation" title=" solar radiation"> solar radiation</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a> </p> <a href="https://publications.waset.org/abstracts/27683/solar-radiation-time-series-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2955</span> Verification of Simulated Accumulated Precipitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nato%20Kutaladze">Nato Kutaladze</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Mikuchadze"> George Mikuchadze</a>, <a href="https://publications.waset.org/abstracts/search?q=Giorgi%20Sokhadze"> Giorgi Sokhadze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Precipitation forecasts are one of the most demanding applications in numerical weather prediction (NWP). Georgia, as the whole Caucasian region, is characterized by very complex topography. The country territory is prone to flash floods and mudflows, quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) at any leading time are very important for Georgia. In this study, advanced research weather forecasting model’s skill in QPF is investigated over Georgia’s territory. We have analyzed several convection parameterization and microphysical scheme combinations for different rainy episodes and heavy rainy phenomena. We estimate errors and biases in accumulated 6 h precipitation using different spatial resolution during model performance verification for 12-hour and 24-hour lead time against corresponding rain gouge observations and satellite data. Various statistical parameters have been calculated for the 8-month comparison period, and some skills of model simulation have been evaluated. Our focus is on the formation and organization of convective precipitation systems in a low-mountain region. Several problems in connection with QPF have been identified for mountain regions, which include the overestimation and underestimation of precipitation on the windward and lee side of the mountains, respectively, and a phase error in the diurnal cycle of precipitation leading to the onset of convective precipitation in model forecasts several hours too early. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extremal%20dependence%20index" title="extremal dependence index">extremal dependence index</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm" title=" false alarm"> false alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20weather%20prediction" title=" numerical weather prediction"> numerical weather prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20precipitation%20forecasting" title=" quantitative precipitation forecasting"> quantitative precipitation forecasting</a> </p> <a href="https://publications.waset.org/abstracts/136165/verification-of-simulated-accumulated-precipitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136165.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2954</span> The Position of Space weather in Africa-Education and Outreach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babagana%20Abubakar">Babagana Abubakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Alhaji%20Kuya"> Alhaji Kuya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although the field of Space weather science is a young field among the space sciences, but yet history has it that activities related to this science began since the year 1859 when the great solar storm happened which resulted in the disruptions of telegraphs operations around the World at that particular time subsequently making it possible for the scientist Richard Carrington to be able to connect the Solar flare observed a day earlier before the great storm and the great deflection of the Earth’s Magnetic field (geometric storm) simultaneous with the telegraph disruption. However years later as at today with the advent of and the coming into existence of the Explorer 1, the Luna 1 and the establishments of the United States International Space Weather Program, International Geophysical Year (IGY) as well as the International Center for Space Weather Sciences and Education (ICSWSE) have made us understand the Space weather better and enable us well define the field of Space weather science. Despite the successes recorded in the development of Space sciences as a whole over the last century and the coming onboard of specialized bodies/programs on space weather like the International Space Weather Program and the ICSWSE, the majority of Africans including institutions, research organizations and even some governments are still ignorant about the existence of theSpace weather science,because apart from some very few countries like South Africa, Nigeria and Egypt among some few others the majority of the African nations and their academic institutions have no knowledge or idea about the existence of this field of Space science (Space weather). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Africa" title="Africa">Africa</a>, <a href="https://publications.waset.org/abstracts/search?q=space" title=" space"> space</a>, <a href="https://publications.waset.org/abstracts/search?q=weather" title=" weather"> weather</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=science" title=" science"> science</a> </p> <a href="https://publications.waset.org/abstracts/18781/the-position-of-space-weather-in-africa-education-and-outreach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18781.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">449</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2953</span> Personalized Infectious Disease Risk Prediction System: A Knowledge Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retno%20A.%20Vinarti">Retno A. Vinarti</a>, <a href="https://publications.waset.org/abstracts/search?q=Lucy%20M.%20Hederman"> Lucy M. Hederman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title="epidemiology">epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20modelling" title=" knowledge modelling"> knowledge modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease" title=" infectious disease"> infectious disease</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a> </p> <a href="https://publications.waset.org/abstracts/55891/personalized-infectious-disease-risk-prediction-system-a-knowledge-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55891.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">242</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2952</span> Agriculture Yield Prediction Using Predictive Analytic Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nagini%20Sabbineni">Nagini Sabbineni</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajini%20T.%20V.%20Kanth"> Rajini T. V. Kanth</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20V.%20Kiranmayee"> B. V. Kiranmayee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture%20yield%20growth" title="agriculture yield growth">agriculture yield growth</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture%20yield%20prediction" title=" agriculture yield prediction"> agriculture yield prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=explorative%20data%20analysis" title=" explorative data analysis"> explorative data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20models" title=" regression models"> regression models</a> </p> <a href="https://publications.waset.org/abstracts/54159/agriculture-yield-prediction-using-predictive-analytic-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54159.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2951</span> Combining Multiscale Patterns of Weather and Sea States into a Machine Learning Classifier for Mid-Term Prediction of Extreme Rainfall in North-Western Mediterranean Sea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pinel%20Sebastien">Pinel Sebastien</a>, <a href="https://publications.waset.org/abstracts/search?q=Bourrin%20Fran%C3%A7ois"> Bourrin François</a>, <a href="https://publications.waset.org/abstracts/search?q=De%20Madron%20Du%20Rieu%20Xavier"> De Madron Du Rieu Xavier</a>, <a href="https://publications.waset.org/abstracts/search?q=Ludwig%20Wolfgang"> Ludwig Wolfgang</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnau%20Pedro"> Arnau Pedro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20hazards" title="extreme hazards">extreme hazards</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=heavy%20rainfall" title=" heavy rainfall"> heavy rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-atmosphere%20modeling" title=" sea-atmosphere modeling"> sea-atmosphere modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=precipitation%20forecasting" title=" precipitation forecasting"> precipitation forecasting</a> </p> <a href="https://publications.waset.org/abstracts/149584/combining-multiscale-patterns-of-weather-and-sea-states-into-a-machine-learning-classifier-for-mid-term-prediction-of-extreme-rainfall-in-north-western-mediterranean-sea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149584.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">135</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2950</span> A Nonstandard Finite Difference Method for Weather Derivatives Pricing Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Clarinda%20Vitorino%20Nhangumbe">Clarinda Vitorino Nhangumbe</a>, <a href="https://publications.waset.org/abstracts/search?q=Fredericks%20Ebrahim"> Fredericks Ebrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Betuel%20Canhanga"> Betuel Canhanga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The price of an option weather derivatives can be approximated as a solution of the two-dimensional convection-diffusion dominant partial differential equation derived from the Ornstein-Uhlenbeck process, where one variable represents the weather dynamics and the other variable represent the underlying weather index. With appropriate financial boundary conditions, the solution of the pricing equation is approximated using a nonstandard finite difference method. It is shown that the proposed numerical scheme preserves positivity as well as stability and consistency. In order to illustrate the accuracy of the method, the numerical results are compared with other methods. The model is tested for real weather data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonstandard%20finite%20differences" title="nonstandard finite differences">nonstandard finite differences</a>, <a href="https://publications.waset.org/abstracts/search?q=Ornstein-Uhlenbeck%20process" title=" Ornstein-Uhlenbeck process"> Ornstein-Uhlenbeck process</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20differential%20equations%20approach" title=" partial differential equations approach"> partial differential equations approach</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20derivatives" title=" weather derivatives"> weather derivatives</a> </p> <a href="https://publications.waset.org/abstracts/169730/a-nonstandard-finite-difference-method-for-weather-derivatives-pricing-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169730.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2949</span> Design of a Standard Weather Data Acquisition Device for the Federal University of Technology, Akure Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Kayode%20Ogunlade">Isaac Kayode Ogunlade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition%20system" title="data acquisition system">data acquisition system</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20device" title=" design device"> design device</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20development" title=" weather development"> weather development</a>, <a href="https://publications.waset.org/abstracts/search?q=predict%20precipitation%20and%20%28FUTA%29%20standard%20device" title=" predict precipitation and (FUTA) standard device"> predict precipitation and (FUTA) standard device</a> </p> <a href="https://publications.waset.org/abstracts/149813/design-of-a-standard-weather-data-acquisition-device-for-the-federal-university-of-technology-akure-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2948</span> Forecasting the Temperature at a Weather Station Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debneil%20Saha%20Roy">Debneil Saha Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast horizon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short%20term%20memory" title=" long short term memory"> long short term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/124787/forecasting-the-temperature-at-a-weather-station-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124787.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2947</span> Climate Change and Extreme Weather: Understanding Interconnections and Implications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Johnstone%20Walubengo%20Wangusi">Johnstone Walubengo Wangusi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate change is undeniably altering the frequency, intensity, and geographic distribution of extreme weather events worldwide. In this paper, we explore the complex interconnections between climate change and extreme weather phenomena, drawing upon research from atmospheric science, geology, and climatology. We examine the underlying mechanisms driving these changes, the impacts on natural ecosystems and human societies, and strategies for adaptation and mitigation. By synthesizing insights from interdisciplinary research, this paper aims to provide a comprehensive understanding of the multifaceted relationship between climate change and extreme weather, informing efforts to address the challenges posed by a changing climate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20weather" title=" extreme weather"> extreme weather</a>, <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20science" title=" atmospheric science"> atmospheric science</a>, <a href="https://publications.waset.org/abstracts/search?q=geology" title=" geology"> geology</a>, <a href="https://publications.waset.org/abstracts/search?q=climatology" title=" climatology"> climatology</a>, <a href="https://publications.waset.org/abstracts/search?q=impacts" title=" impacts"> impacts</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation" title=" adaptation"> adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=mitigation" title=" mitigation"> mitigation</a> </p> <a href="https://publications.waset.org/abstracts/184530/climate-change-and-extreme-weather-understanding-interconnections-and-implications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">64</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2946</span> Hourly Solar Radiations Predictions for Anticipatory Control of Electrically Heated Floor: Use of Online Weather Conditions Forecast</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helene%20Thieblemont">Helene Thieblemont</a>, <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Haghighat"> Fariborz Haghighat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anticipatory%20control" title="anticipatory control">anticipatory control</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20radiation%20forecast" title=" solar radiation forecast"> solar radiation forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20storage" title=" thermal storage"> thermal storage</a> </p> <a href="https://publications.waset.org/abstracts/61503/hourly-solar-radiations-predictions-for-anticipatory-control-of-electrically-heated-floor-use-of-online-weather-conditions-forecast" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61503.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">271</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2945</span> Mean Monthly Rainfall Prediction at Benina Station Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasan%20G.%20Elmazoghi">Hasan G. Elmazoghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aisha%20I.%20Alzayani"> Aisha I. Alzayani</a>, <a href="https://publications.waset.org/abstracts/search?q=Lubna%20S.%20Bentaher"> Lubna S. Bentaher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=climatic%20variables" title=" climatic variables"> climatic variables</a> </p> <a href="https://publications.waset.org/abstracts/17036/mean-monthly-rainfall-prediction-at-benina-station-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17036.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">488</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2944</span> Automatic Flood Prediction Using Rainfall Runoff Model in Moravian-Silesian Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sir">B. Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Podhoranyi"> M. Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kuchar"> S. Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kocyan"> T. Kocyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood">flood</a>, <a href="https://publications.waset.org/abstracts/search?q=HEC-HMS" title=" HEC-HMS"> HEC-HMS</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=runoff" title=" runoff "> runoff </a> </p> <a href="https://publications.waset.org/abstracts/20151/automatic-flood-prediction-using-rainfall-runoff-model-in-moravian-silesian-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">394</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2943</span> Comparison of Different Reanalysis Products for Predicting Extreme Precipitation in the Southern Coast of the Caspian Sea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parvin%20Ghafarian">Parvin Ghafarian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Mohammadpur%20Panchah"> Mohammadreza Mohammadpur Panchah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehri%20Fallahi"> Mehri Fallahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synoptic patterns from surface up to tropopause are very important for forecasting the weather and atmospheric conditions. There are many tools to prepare and analyze these maps. Reanalysis data and the outputs of numerical weather prediction models, satellite images, meteorological radar, and weather station data are used in world forecasting centers to predict the weather. The forecasting extreme precipitating on the southern coast of the Caspian Sea (CS) is the main issue due to complex topography. Also, there are different types of climate in these areas. In this research, we used two reanalysis data such as ECMWF Reanalysis 5th Generation Description (ERA5) and National Centers for Environmental Prediction /National Center for Atmospheric Research (NCEP/NCAR) for verification of the numerical model. ERA5 is the latest version of ECMWF. The temporal resolution of ERA5 is hourly, and the NCEP/NCAR is every six hours. Some atmospheric parameters such as mean sea level pressure, geopotential height, relative humidity, wind speed and direction, sea surface temperature, etc. were selected and analyzed. Some different type of precipitation (rain and snow) was selected. The results showed that the NCEP/NCAR has more ability to demonstrate the intensity of the atmospheric system. The ERA5 is suitable for extract the value of parameters for specific point. Also, ERA5 is appropriate to analyze the snowfall events over CS (snow cover and snow depth). Sea surface temperature has the main role to generate instability over CS, especially when the cold air pass from the CS. Sea surface temperature of NCEP/NCAR product has low resolution near coast. However, both data were able to detect meteorological synoptic patterns that led to heavy rainfall over CS. However, due to the time lag, they are not suitable for forecast centers. The application of these two data is for research and verification of meteorological models. Finally, ERA5 has a better resolution, respect to NCEP/NCAR reanalysis data, but NCEP/NCAR data is available from 1948 and appropriate for long term research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synoptic%20patterns" title="synoptic patterns">synoptic patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=heavy%20precipitation" title=" heavy precipitation"> heavy precipitation</a>, <a href="https://publications.waset.org/abstracts/search?q=reanalysis%20data" title=" reanalysis data"> reanalysis data</a>, <a href="https://publications.waset.org/abstracts/search?q=snow" title=" snow"> snow</a> </p> <a href="https://publications.waset.org/abstracts/112447/comparison-of-different-reanalysis-products-for-predicting-extreme-precipitation-in-the-southern-coast-of-the-caspian-sea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112447.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2942</span> Pricing the Risk Associated to Weather of Variable Renewable Energy Generation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20M.%20Uribe">Jorge M. Uribe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a methodology for setting the price of an insurance contract targeted to manage the risk associated with weather conditions that affect variable renewable energy generation. The methodology relies on conditional quantile regressions to estimate the weather risk of a solar panel. It is illustrated using real daily radiation and weather data for three cities in Spain (Valencia, Barcelona and Madrid) from February 2/2004 to January 22/2019. We also adapt the concepts of value at risk and expected short fall from finance to this context, to provide a complete panorama of what we label as weather risk. The methodology is easy to implement and can be used by insurance companies to price a contract with the aforementioned characteristics when data about similar projects and accurate cash flow projections are lacking. Our methodology assigns a higher price to an insurance product with the stated characteristics in Madrid, compared to Valencia and Barcelona. This is consistent with Madrid showing the largest interquartile range of operational deficits and it is unrelated to the average value deficit, which illustrates the importance of our proposal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=insurance" title="insurance">insurance</a>, <a href="https://publications.waset.org/abstracts/search?q=weather" title=" weather"> weather</a>, <a href="https://publications.waset.org/abstracts/search?q=vre" title=" vre"> vre</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk "> risk </a> </p> <a href="https://publications.waset.org/abstracts/118539/pricing-the-risk-associated-to-weather-of-variable-renewable-energy-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118539.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2941</span> Comparison of Power Generation Status of Photovoltaic Systems under Different Weather Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhaojun%20Wang">Zhaojun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zongdi%20Sun"> Zongdi Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinqin%20Cui"> Qinqin Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingwan%20Ren"> Xingwan Ren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on multivariate statistical analysis theory, this paper uses the principal component analysis method, Mahalanobis distance analysis method and fitting method to establish the photovoltaic health model to evaluate the health of photovoltaic panels. First of all, according to weather conditions, the photovoltaic panel variable data are classified into five categories: sunny, cloudy, rainy, foggy, overcast. The health of photovoltaic panels in these five types of weather is studied. Secondly, a scatterplot of the relationship between the amount of electricity produced by each kind of weather and other variables was plotted. It was found that the amount of electricity generated by photovoltaic panels has a significant nonlinear relationship with time. The fitting method was used to fit the relationship between the amount of weather generated and the time, and the nonlinear equation was obtained. Then, using the principal component analysis method to analyze the independent variables under five kinds of weather conditions, according to the Kaiser-Meyer-Olkin test, it was found that three types of weather such as overcast, foggy, and sunny meet the conditions for factor analysis, while cloudy and rainy weather do not satisfy the conditions for factor analysis. Therefore, through the principal component analysis method, the main components of overcast weather are temperature, AQI, and pm2.5. The main component of foggy weather is temperature, and the main components of sunny weather are temperature, AQI, and pm2.5. Cloudy and rainy weather require analysis of all of their variables, namely temperature, AQI, pm2.5, solar radiation intensity and time. Finally, taking the variable values in sunny weather as observed values, taking the main components of cloudy, foggy, overcast and rainy weather as sample data, the Mahalanobis distances between observed value and these sample values are obtained. A comparative analysis was carried out to compare the degree of deviation of the Mahalanobis distance to determine the health of the photovoltaic panels under different weather conditions. It was found that the weather conditions in which the Mahalanobis distance fluctuations ranged from small to large were: foggy, cloudy, overcast and rainy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fitting" title="fitting">fitting</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title=" Mahalanobis distance"> Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=SPSS" title=" SPSS"> SPSS</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/97522/comparison-of-power-generation-status-of-photovoltaic-systems-under-different-weather-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97522.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2940</span> Performance of Photovoltaic Thermal Greenhouse Dryer in Composite Climate of India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Tiwari">G. N. Tiwari</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyam"> Shyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Photovoltaic thermal (PVT) roof type greenhouse dryer installed above the wind tower of SODHA BERS COMPLEX, Varanasi has been analyzed for all types of weather conditions. The product to be dried has been kept at three different trays. The upper tray receives energy from the PV cover while the bottom tray receives thermal energy from the hot air of the wind tower. The annual energy estimation has been done for the all types of weather condition of composite climate of northern India. It has been found that maximum energy saving is observed for c type of weather condition whereas minimum energy saving is observed for a type of weather condition. The energy saving on overall thermal energy basis and exergy basis are 1206.8 kWh and 360 kWh respectively for c type of weather condition. The energy saving from all types of weather condition are found to be 3175.3 kWh and 957.6 kWh on overall thermal energy and overall exergy basis respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exergy" title="exergy">exergy</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse" title=" greenhouse"> greenhouse</a>, <a href="https://publications.waset.org/abstracts/search?q=photovoltaic%20thermal" title=" photovoltaic thermal"> photovoltaic thermal</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20dryer" title=" solar dryer"> solar dryer</a> </p> <a href="https://publications.waset.org/abstracts/36908/performance-of-photovoltaic-thermal-greenhouse-dryer-in-composite-climate-of-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2939</span> Validation of Visibility Data from Road Weather Information Systems by Comparing Three Data Resources: Case Study in Ohio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fan%20Ye">Fan Ye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RWIS" title="RWIS">RWIS</a>, <a href="https://publications.waset.org/abstracts/search?q=visibility%20distance" title=" visibility distance"> visibility distance</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20visibility" title=" low visibility"> low visibility</a>, <a href="https://publications.waset.org/abstracts/search?q=adverse%20weather" title=" adverse weather"> adverse weather</a> </p> <a href="https://publications.waset.org/abstracts/67942/validation-of-visibility-data-from-road-weather-information-systems-by-comparing-three-data-resources-case-study-in-ohio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">249</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2938</span> Probabilistic Crash Prediction and Prevention of Vehicle Crash</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lavanya%20Annadi">Lavanya Annadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahimeh%20Jafari"> Fahimeh Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=road%20safety" title="road safety">road safety</a>, <a href="https://publications.waset.org/abstracts/search?q=crash%20prediction" title=" crash prediction"> crash prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=exploratory%20analysis" title=" exploratory analysis"> exploratory analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/148423/probabilistic-crash-prediction-and-prevention-of-vehicle-crash" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148423.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2937</span> Power Grid Line Ampacity Forecasting Based on a Long-Short-Term Memory Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang-Yao%20Zheng">Xiang-Yao Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Jen-Cheng%20Wang"> Jen-Cheng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Joe-Air%20Jiang"> Joe-Air Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Improving the line ampacity while using existing power grids is an important issue that electricity dispatchers are now facing. Using the information provided by the dynamic thermal rating (DTR) of transmission lines, an overhead power grid can operate safely. However, dispatchers usually lack real-time DTR information. Thus, this study proposes a long-short-term memory (LSTM)-based method, which is one of the neural network models. The LSTM-based method predicts the DTR of lines using the weather data provided by Central Weather Bureau (CWB) of Taiwan. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed LSTM-based prediction method. A case study that targets the 345 kV power grid of TaiPower in Taiwan is utilized to examine the performance of the proposed method. The simulation results show that the proposed method is useful to provide the information for the smart grid application in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20dispatch" title="electricity dispatch">electricity dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20ampacity%20prediction" title=" line ampacity prediction"> line ampacity prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20thermal%20rating" title=" dynamic thermal rating"> dynamic thermal rating</a>, <a href="https://publications.waset.org/abstracts/search?q=long-short-term%20memory%20neural%20network" title=" long-short-term memory neural network"> long-short-term memory neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title=" smart grid"> smart grid</a> </p> <a href="https://publications.waset.org/abstracts/63755/power-grid-line-ampacity-forecasting-based-on-a-long-short-term-memory-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63755.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weather%20prediction&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weather%20prediction&page=3">3</a></li> <li class="page-item"><a class="page-link" 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