Spatial and Temporal Aware Graph Convolutional Network for Flood Forecasting
Jun Feng, Zhongyi Wang, Yirui Wu, Yuqi Xi
Abstract
Intelligent flood forecasting systems provide an effective means to forecast flood disaster. Accurate flood flow value prediction is a huge challenge since it's influenced by both spatial and temporal relationship among flood factors. Popular deep learning structures like Long Short-Term Memory (LSTM) network lacks abilities of modeling the spatial correlations of hydrological data, thus cannot yield satisfactory prediction results. Moreover, not all the temporal information is always valuable for flood forecasting. In this paper, we proposed a novel spatial and temporal aware Graph Convolution Network (ST-GCN) for flood prediction, which is capable to extract spatial-temporal information from raw flood data. Moreover, a temporal attention mechanism is introduced to weight the importance of different time steps, thus involving global temporal information to improve flood prediction accuracy. Compared with the existing methods, results on two self-collected datasets show that ST-GCN greatly improves the prediction performance.