Prediction of weather forecasting using artificial neural networks
Abstract Currently, weather forecasting is the most discussed topic by social and economic activists. It is also attracting wide spread interest due to its application in various public and private sectors that include marine, agriculture, air traffic, and forestry. Recent developments have made climatic changes happen at a dramatic rate, making old methods of weather forecasting less effective, more hectic, and unreliable. Improved and efficient methods of weather prediction are needed to overcome these difficulties. This paper describes machine learning approaches using artificial neural networks to predict the weather of a particular city and compare the different weather conditions in different cities. We demonstrate empirically that artificial neural networks produce incredibly lower deviations than GDAS evaluation. Hence the prediction of nearly accurate results for weather forecasts on a daily basis.
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Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología
2023
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oai:scielo:S1665-642320230002002052024-08-27Prediction of weather forecasting using artificial neural networksAjina,A.Jaya,Christiyan K. G.Bhat,Dheerej NSaxena,Kanishk Artificial neural networks AI weather forecast Machine learning Weather forecasting Weather prediction Abstract Currently, weather forecasting is the most discussed topic by social and economic activists. It is also attracting wide spread interest due to its application in various public and private sectors that include marine, agriculture, air traffic, and forestry. Recent developments have made climatic changes happen at a dramatic rate, making old methods of weather forecasting less effective, more hectic, and unreliable. Improved and efficient methods of weather prediction are needed to overcome these difficulties. This paper describes machine learning approaches using artificial neural networks to predict the weather of a particular city and compare the different weather conditions in different cities. We demonstrate empirically that artificial neural networks produce incredibly lower deviations than GDAS evaluation. Hence the prediction of nearly accurate results for weather forecasts on a daily basis.info:eu-repo/semantics/openAccessUniversidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y TecnologíaJournal of applied research and technology v.21 n.2 20232023-01-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232023000200205en10.22201/icat.24486736e.2023.21.2.1698 |
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Ajina,A. Jaya,Christiyan K. G. Bhat,Dheerej N Saxena,Kanishk |
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Ajina,A. Jaya,Christiyan K. G. Bhat,Dheerej N Saxena,Kanishk Prediction of weather forecasting using artificial neural networks |
| author_facet |
Ajina,A. Jaya,Christiyan K. G. Bhat,Dheerej N Saxena,Kanishk |
| author_sort |
Ajina,A. |
| title |
Prediction of weather forecasting using artificial neural networks |
| title_short |
Prediction of weather forecasting using artificial neural networks |
| title_full |
Prediction of weather forecasting using artificial neural networks |
| title_fullStr |
Prediction of weather forecasting using artificial neural networks |
| title_full_unstemmed |
Prediction of weather forecasting using artificial neural networks |
| title_sort |
prediction of weather forecasting using artificial neural networks |
| description |
Abstract Currently, weather forecasting is the most discussed topic by social and economic activists. It is also attracting wide spread interest due to its application in various public and private sectors that include marine, agriculture, air traffic, and forestry. Recent developments have made climatic changes happen at a dramatic rate, making old methods of weather forecasting less effective, more hectic, and unreliable. Improved and efficient methods of weather prediction are needed to overcome these difficulties. This paper describes machine learning approaches using artificial neural networks to predict the weather of a particular city and compare the different weather conditions in different cities. We demonstrate empirically that artificial neural networks produce incredibly lower deviations than GDAS evaluation. Hence the prediction of nearly accurate results for weather forecasts on a daily basis. |
| publisher |
Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología |
| publishDate |
2023 |
| url |
http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232023000200205 |
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