Abstract Reservoir water level forecasting is essential for flood control and water resource optimization, yet remains challenging due to the nonlinear, non-stationary, and multi-scale nature of hydrological processes. This paper proposes a hybrid deep learning model (GLA-GRU-LSTM) that integrates GRU and LSTM with dual attention mechanisms to capture both long-term trends and short-term fluctuations in water level dynamics. The global attention module extracts long-range dependencies across the entire sequence, while the local attention module focuses on abrupt changes within a recent temporal window. The model was evaluated using ten-year (2014–2023) hydrometeorological data from Yefan Reservoir, China, and compared against SVR, LSTM, CNN-LSTM, Transformer, TFT, and RFMTrans. For 7 day forecasting, GLA-GRU-LSTM achieved NSE of 0. 934 and RMSE of 1. 647, outperforming Transformer (NSE = 0. 921) and TFT (NSE = 0. 916). For 30 day forecasting, it maintained superior performance (NSE = 0. 807, RMSE = 2. 773), with the performance gap widening as forecast horizon extended. Ablation studies confirm that performance gains stem from synergistic integration of GRU-LSTM hybridization and dual attention, not merely increased parameterization. Statistical significance tests (Diebold-Mariano, p < 0. 05) and bootstrap confidence intervals confirm the robustness of improvements. The proposed framework provides an accurate and interpretable approach for multi-scale reservoir water level forecasting, with potential applications in operational water management.
Yang et al. (Fri,) studied this question.