Interpretable spatial-temporal attention convolutional network for rainfall forecasting

The interpretability of rainfall forecasting models is a major challenge in the field of artificial intelligence. Its importance is equal to the evaluation of model accuracy. Owing to the uncertainty and nonlinearity of the rainfall forecasting process, existing hydrological rainfall forecasting models often have low prediction robustness, and the machine learning method ignores the influence of physical factors in the surrounding areas; thus, the physical interpretability of the model is inferior. This study proposes an interpretable spatial-temporal attention convolution network for rainfall interval forecasting (IDSTA-TCN). To analyze and mine the uncertainty of input data more effectively, we enhanced a variational mode decomposition (EBO-VMD) algorithm based on Bayesian optimization to decompose the input data. To improve the performance of the original temporal convolution network (TCN) unit in rainfall forecasting and to effectively use the spatial-temporal information of rainfall forecasting data, we improved the spatial-temporal attention mechanism, dynamically combined the geographical information of the meteorological stations in the surrounding area to assist the network unit in better mining the nonlinear impact of historical input information on current rainfall forecasting, and improved the prediction performance of the IDSTA-TCN model. Finally, we designed a more reasonable error factor adjustment interval output strategy to better quantify the impact of prediction errors on the rainfall interval prediction results. Moreover, we conducted a series of experiments to compare and analyze the accuracy of each model according to the most commonly used evaluation indicators in the field of rainfall forecasting. The results demonstrate that the proposed IDSTA-TCN model is superior to the baseline models in most cases. We preliminarily analyzed the spatial-temporal attention weights, which explained the physical rationality of our model.