Research on runoff prediction method based on multigraph spatiotemporal feature fusion

To improve the accuracy of watershed runoff prediction and address the lack of interpretability in data-driven hydrological models, we propose a spatiotemporal prediction model, named the multigraph attention mechanism with gated recurrent unit (MGAT–MIC–GRU). This model leverages a multigraph attention mechanism, combining graph attention network (GAT) and gated recurrent neural network (GRU), for runoff forecasting. Firstly, employing hydrological data from the Qishui River Basin, a graph neural network, is utilized to extract the topological structure of the watershed site and generate feature vectors. Secondly, according to the characteristics of hydrological time series data, a multigraph attention runoff spatiotemporal prediction model based on MIC mechanism is established to predict the runoff of the basin. Finally, we conduct comparative experiments over multiple forecasting horizons to evaluate the predictive performance of various models, including the MGAT–MIC–GRU model. The research findings demonstrate that our proposed model improves the accuracy of runoff prediction.