Exploring the Potential of Deep Learning Models in Weather Prediction: Case Studies on Sandy Weather, Wind Speed, and Rainfall

Abstract

Weather prediction is one of the major focuses of today’s research, and researchers have found that traditional prediction models are limited in the ability of weather prediction, while deep learning has a relatively stronger performance in weather prediction, however, there are still blanks in the research on the accuracy and applicability of the models. Therefore, this study aims to explore the application of different deep-learning models in weather prediction and make recommendations accordingly. In this paper, deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) will be investigated for case studies in the prediction of three different kinds of weather, namely, sandy and dusty weather, wind speed, and rainfall, respectively, along with a summary of the strengths and weaknesses of the different models. The results show that deep learning models can accurately predict future weather conditions by summarizing and analyzing the temporal and spatial characteristics of the data, while the combination of different models can further improve the model performance according to their advantages. In summary, this study provides new ideas for the further development of weather prediction and provides an important reference for future related research.