Graph Convolution Based Spatial-Temporal Attention LSTM Model for Flood Forecasting
Jun Feng, Haichao Sha, Yukai Ding, Le Yan, Zhangheng Yu
Abstract
Accurate flood forecast is crucial to ensure economic and ecological environment safety. Due to the complex factors affecting flood runoff in the small and medium-sized river basins, the traditional model cannot yield satisfactory prediction results. In this paper, we propose a novel Graph Convolution based spatial-temporal Attention LSTM(AGCLSTM) network to tackle the time series prediction problem in the flood forecasting domain. To be specific, our model contains two major modules: 1) the spatial-temporal GCN module with the dropedge mechanism which adequately captures the spatial and temporal characteristics of topological river graphs; 2) the spatial-temporal LSTM module to effectively extract temporal and spatial dynamic correlation in time series hydrological data. Experiments show that our model has excellent performance in flood peak prediction and flow calibration compared with the existing machine learning methods.