Joint spatial and temporal modeling for hydrological prediction

The accurate and timely estimation of river discharge plays an important role in hydrological modeling, especially for avoiding the consequences of flood events. The majority of existing work on hydrologic prediction focuses on modeling the inherent physical process for specific river basins, while the geographic-connections between rivers are largely ignored. Geographically connected rivers provide rich spatial information that can be used to predict discharge amounts. In this paper, we study a novel problem of exploiting both temporal patterns and spatial connections for hydrological prediction. We construct three relationship graphs for hydrological gauges in the study area: the hydraulic distance graph, the Euclidean distance graph and the correlation graph. We fuse these graphs into one hydrological network graph, and propose a novel framework ST-Hydro which exploits Graph Convolutional Networks (GCN) for learning the spatial feature representations, and Recurrent Neural Networks with carefully designed activation functions for capturing temporal features simultaneously for hydrological prediction. Experimental results on real world data set demonstrate that the proposed framework can predict the river discharge effectively and at an early stage.