CNN-BiLSTM Daily Precipitation Prediction Based on Attention Mechanism

Abstract

Accurate daily precipitation forecasting is crucial for the rational utilization of water resources and the prediction of flood disasters. To address the low reliability and low prediction accuracy of existing daily precipitation prediction models based on deep learning which arise from the nonlinear and non-stationary characteristics of surface precipitation data, this paper first employs the principal component analysis (PCA) method to extract the principal components of the original data. Given that the convolutional neural network (CNN) is adept at capturing spatial dependencies, bidirectional long short-term memory (Bi-LSTM, a variant of long short-term memory (LSTM)) can capture the long-term dependence of time-series data, and the attention mechanism allows the model to focus on the more important features of the input data. A PCA-CNN-BiLSTM-Attention fusion neural network was constructed. Taking Kunming, China as the study area, the experimental results demonstrate that the Nash efficiency coefficient of the proposed model reaches 0.993, which is 15.3% and 12.6% higher than that of the CNN-LSTM and CNN-BiLSTM models, respectively. This indicates high prediction accuracy and provides an effective and feasible method for daily precipitation prediction.