Spatiotemporal MultiWaveNet for Efficiently Generating Environmental Spatiotemporal Series

Real-time and accurate modeling of environmental variables in a specific region is of great significance to human production activities. In the era of intelligence, there is an increasing demand for accuracy and real-time performance of modeling environmental variables. However, due to the lack of a priori knowledge of spatiotemporal features and the high computational complexity, the existing deep learning methods have poor accuracy, and the real-time performance needs to be improved. To solve the above problems, this article proposes a novel SpatioTemporal MultiWaveNet (STMWNet) based on 3-D convolution for end-to-end environmental spatiotemporal variables generation. The Spatiotemporal MultiWave Layer (STMW Layer) is designed based on the principle of wavelet transform to extract multifrequency spatiotemporal features from the series. The multiscale embedding module (MSEM), time infofusion module (TIFM), and embedding integration module (EIM) are proposed to further extract and integrate the spatiotemporal features to improve the accuracy of the model. Considering the short-term and long-term temporal characteristics of spatiotemporal series, the date encoding strategy and the cross-attention head are proposed to integrate relative and absolute date information. The improved loss function is proposed to optimize the model learning. Comparative experiments on three datasets with different physical fields, different spatial scales, and resolutions show that the proposed method can accurately generate the mesoscale environmental spatiotemporal variables in real time for different tasks with the generation error lower than other methods by 1.02%~83.52% on root mean square error (RMSE), and 1.67%~83.68% on mean absolute error (MAE). Further experiments show that the proposed method can maintain its performance in practical applications.