Rainfall prediction based on CNN-LSTM model under sliding window
Rui Yuan
Abstract
As monthly rainfall is a long-term and complex process, this study utilized the characteristics of the sliding window to replace the current month’s data with the multi-month rainfall data set within the sliding window for predictive analysis. At the same time, a CNN-LSTM model was used to assist in the prediction of monthly rainfall, and the results were compared with those obtained using LSTM and GRU. Due to the new division of data through the sliding window method, the input data dimension to the CNN-LSTM model was relatively small. Therefore, data augmentation was employed to enhance the overall predictive ability of the model. This study selected 43 years (1980–2022) of meteorological data from the Yichun Station and Zhangye Station in NOAA. The following evaluation indicators were used to assess the model’s performance: NSE, RSR, RMSE, and R. The results demonstrated that the predictive ability of the CNN-LSTM model was superior to that of LTSM and GRU. Specifically, R and NSE increased significantly (20%–30%), while RSR and RMSE decreased slightly (6%–13%). The CNN-LSTM model proposed in this study, based on the sliding window, enabling more accurate predictions of monthly rainfall.