A Short‐Term Regional Precipitation Prediction Model Based on Wind‐Improved Spatiotemporal Convolutional Network

Abstract Accurate precipitation prediction can help decision makers judge the trend of climate change and formulate more effective measures, and prevent flood and drought disasters. In this paper, we propose a short‐term regional precipitation prediction model based on wind‐improved spatiotemporal convolutional network. Among them, the improved graph convolution network integrates the effects of wind direction and geographic location at past moments to capture the spatial dependence, whilst the gated recurrent unit captures the temporal dependence by learning the dynamic changes of data. The spatio‐temporal memory flow module and attention module are added to capture spatial deformation and temporal variation more accurately, thereby better matching the physical properties of precipitation. The proposed model achieves better prediction results on real data sets. Experiments show that our method is better at extracting the spatio‐temporal information of precipitation data and capturing its time dependence and spatial correlation.