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<p>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 RFM_Trans. For 7&#xa0;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&#xa0;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 &lt; 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.</p>

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A study on the GRU-LSTM hybrid model with dual attention mechanism for reservoir water level forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Guangying Yang,
  • Fan Zhou

摘要

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 RFM_Trans. 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.