CodGRU: Covariate gated attention GRU for short term runoff forecasting

Runoff simulation and forecasting are critical for effective watershed management, yet their complexity and nonlinear characteristics pose significant challenges. The generation of runoff is the result of the interaction between multiple factors such as precipitation, soil moisture, topography, and human activities, making this dynamic process extremely complex and difficult to model accurately. Traditional models are often based on an understanding of these natural processes but may fall short when dealing with complex real-world scenarios. To address these issues, this study has developed a dynamic gated feature selection model with attention mechanisms (CodGRU), specifically designed for multi-watershed hourly runoff forecasting. By dynamically adjusting the weights of input variables and selecting the most important features, CodGRU can better capture the key factors influencing runoff, thereby improving the accuracy of forecasting. The test results of five watersheds on the real dataset show that the CodGRU model performs excellently in multi-watershed hourly-scale forecasting, outperforming the existing mainstream models and providing reliable support for water resource management and disaster forecasting and mitigation.