Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network

Abstract

Runoff prediction plays an important role in the construction of intelligent hydraulic engineering. Most of the existing deep learning runoff prediction models use recurrent neural networks for single-step prediction of a single time series, which mainly model the temporal features and ignore the river convergence process within a watershed. In order to improve the accuracy of runoff prediction, a dynamic spatiotemporal graph neural network model (DSTGNN) is proposed considering the interaction of hydrological stations. The sequences are first input to the spatiotemporal block to extract spatiotemporal features. The temporal features are captured by the long short-term memory network (LSTM) with the self-attention mechanism. Then, the upstream and downstream distance matrices are constructed based on the river network topology in the basin, the dynamic matrix is constructed based on the runoff sequence, and the spatial dependence is captured by combining the above two matrices through the diffusion process. After that, the residual sequences are input to the next layer by the decoupling block, and, finally, the prediction results are output after multi-layer stacking. Experiments are conducted on the historical runoff dataset in the Upper Delaware River Basin, and the MAE, MSE, MAPE, and NSE were the best compared with the baseline model for forecasting periods of 3 h, 6 h, and 9 h. The experimental results show that DSTGNN can better capture the spatiotemporal characteristics and has higher prediction accuracy.