Weather Forecasting Models Based on Deep Learning Techniques: A Survey

Abstract

Weather forecasting is inherently complex; however, scientists are endeavoring to leverage the latest computational technologies to enhance its accuracy. By using huge amounts of data, scientists hope to be able to predict potential weather patterns as accurately as possible. If their efforts succeed, meteorologists may be able to predict major events for years or even decades to come. This could provide tremendous potential to save the lives of many people from severe weather conditions such as hurricanes, droughts, and floods. Many different methods and algorithms for deep learning and machine learning have been used to predict weather, including RNN, LSTM, GRU, RN-Net, the CRNN model, and BLSTM-GRU, and machine learning algorithms including ANN, SVM, Random Forest (RF), and K-Nearest Neighbor. . In this survey, we will briefly summarize the methods used to build models to predict climate change.