A Hybrid Model of SARIMA and GRU in Water Level Forecasting: Case Study in Ankhe Reservoir, Vietnam

Reservoir water levels exhibit significant irregular fluctuations due to climate change and intensified water resource exploitation. To improve forecasting accuracy, this study introduces SARIMA-Embedded GRU (SE-GRU), a novel hybrid model that directly embeds statistical time-series components-seasonal buffers, trend differencing gates, and ARMA structures into GRU recurrent units. Unlike conventional two-stage hybrid methods, SE-GRU simultaneously captures linear statistical patterns and nonlinear dependencies through end-to-end optimization, enhancing both accuracy and interpretability. Evaluated on An Khe reservoir data, the proposed model achieves superior performance (MAE = 0.001, MSE = 0.014, RMSE = 0.041), outperforming standalone SARIMA, GRU, and other baseline models, demonstrating the effectiveness of integrated architectural design for reservoir water level forecasting.