Deep Reinforcement Learning Based Rainfall Prediction Network for Adaptive Spatio-Temporal Rainfall Prediction

Abstract

Rainfall forecasting plays a crucial role in water resource management, agriculture, disaster prevention, and climate monitoring. Nevertheless, proper forecasting of rainfalls is a difficult task because of the multifaceted, nonlinear, and spatio-temporal character of meteorological information and other environmental elements like astronomical effects (solar and lunar cycles). Existing statistical and Machine Learning (ML) models often fail to effectively capture both spatial and temporal dependencies, leading to reduced prediction accuracy. Consequently, this research seeks to come up with a superior rainfall forecasting model that enhances the accuracy of predictions with the incorporation of Deep Learning (DL) and Reinforcement Learning (RL) algorithms. Its primary aims include modeling spatial-temporal patterns of precipitation, eliciting useful meteorological characteristics and using an adaptive learning process to achieve an optimal prediction. In order to do that, a Deep Reinforcement Learning based Rainfall Prediction Network (DRLRPN) is proposed, incorporating Convolutional Long Short-Term Memory (ConvLSTM) networks, whereby spatio-temporal feature extraction is done, and Deep Reinforcement Learning (DRL) agency, where action optimization is made based on rewards. Several meteorological datasets were pre-processed and made normalized and then trained and evaluated the model within a Python environment. The experiment outcomes illustrate that the proposed model can greatly enhance the performance of rainfall prediction in comparison with the current ML and DL methods. The prediction accuracy of the DRLRPN of 99.90 % is a higher performance when compared to models like GRU, LSTM, U-Net, and GAN. To sum up, the combination of ConvLSTM and DRL is an effective method to improve the accuracy of the spatio-temporal forecasting of rainfall and offer a trustworthy framework of future climate prediction systems.