GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal Attention Graph Convolution Networks

Jin Lu, Zaipeng Xie, Jiayu Chen, Maohua Li, Chenghong Xu, Hongli Cao

Abstract: Runoff prediction is essential for flood forecasting, irrigation planning, and sustainable water resource management. However, accurate predictions can be challenging due to the involvement of multiple variables. This paper presents a novel Graph Convolution-based Spatial-temporal Attention LSTM Multi-Task learning (GC-SALM) model for accurate runoff predictions. Our approach combines a multilayer neural network and an attention mechanism for enhanced generalization performance. The GC-SALM model employs spatial attention and graph convolutional networks to discern local and global spatial patterns, while temporal attention and LSTM are utilized to capture temporal characteristics within extended sequences. Experimental results reveal that the proposed model outperforms six state-of-the-art methods in runoff prediction and flow calibration, emphasizing its potential for real-world hydrological applications.