{"title":"Regional Heatwave Prediction Using deep learning based Recurrent Neural Network","authors":"Saqiba Juna, Sanam Narejo, M. M. Jawaid","doi":"10.1109/ICETECC56662.2022.10069760","DOIUrl":null,"url":null,"abstract":"The increased frequency and severity of heatwaves are a result of global warming’s increasing temperatures. The performance of various reliable prediction models has decreased because of changes in the world environment. LSTM neural networks, among other deep learning-based recurrent neural network algorithms, are used to develop a reliable model for the prediction of heatwave maximum temperature. In this research, we have developed a percentile-based threshold over the predicted maximum temperature to forecast heatwaves using LSTM based predictive model. The LSTM algorithm was applied in this study to determine the approximate maximum temperature for a severe heatwave. The obtained results demonstrate that the proposed percentile approach based on the deep learning LSTM model can solve this issue quickly and effectively.","PeriodicalId":364463,"journal":{"name":"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETECC56662.2022.10069760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The increased frequency and severity of heatwaves are a result of global warming’s increasing temperatures. The performance of various reliable prediction models has decreased because of changes in the world environment. LSTM neural networks, among other deep learning-based recurrent neural network algorithms, are used to develop a reliable model for the prediction of heatwave maximum temperature. In this research, we have developed a percentile-based threshold over the predicted maximum temperature to forecast heatwaves using LSTM based predictive model. The LSTM algorithm was applied in this study to determine the approximate maximum temperature for a severe heatwave. The obtained results demonstrate that the proposed percentile approach based on the deep learning LSTM model can solve this issue quickly and effectively.