Substation flood precipitation forecasting model based on spatio-temporal sequence UNet depth network

作者：Jiaying Ke, Shubo Chen, Xiaoming Chuai, Degui Yao · 年份：2024 · DOI：10.1145/3674225.3674344 · 研究领域：Advanced Computational Techniques and Applications、Evaluation Methods in Various Fields、Environmental and Agricultural Sciences

Extreme climate change can intensify precipitation events in local areas. In order to predict the future precipitation intensity in the area around the substation within a short period of time, a forecasting model based on a spatio-temporal sequence depth network is proposed in this paper. The precipitation forecast is formulated as a spatio-temporal sequence forecasting problem, in which both the input and forecast targets are spatio-temporal sequences. The input-to-state and state-to-state transformations are performed using the convolutional structure in Unet, and an end-to-end trainable model is built for the precipitation forecasting problem. Experiments conducted on radar images from 2019 to 2022 show that the proposed method can better capture spatio-temporal correlations and outperforms fully connected long- and short-term memory networks and convolutional neural networks for short-time precipitation forecasting in areas around substations.