Research on rainfall prediction based on LSTM, RF and SVM models

Abstract

In all kinds of weather events, rainfall plays a vital role in human production and life. China has a vast territory and complex topography, which is severely affected by rainfall. Landslides, mudslides, and floods have occurred in many parts of the country, and the economy and agriculture are affected. The damage is severe. Therefore, it is very important to accurately predict rainfall. This article proposes three rainfall prediction models, which are respectively constructed of Random Forests (RF) prediction models, Support Vector Machine (SVM) prediction models, and long and short-term memory. The network (Long Short-Term Memory, LSTM) prediction model is used for precipitation prediction in Yibin City, Sichuan Province, and the prediction results of the three prediction models are compared. The experiment found that the average absolute error (MAE) of the RF, SVM, and LSTM prediction models were 0.085, 0.089, 0.061, the mean square error (MSE) were 0.016, 0.015, and 0.010, and the average absolute percentage error (MAPE) were 66.38, 79.27, 50.62. Among the three prediction models, the prediction error of LSTM is the smallest, indicating that the accuracy of LSTM model in predicting rainfall is higher than that of RF and SVM models.