Document Type : Research Paper
Authors
1 Department of Range and Watershed Management / Faculty of Agricultural sciencesd and Natural Resources / Gonbad Kavous University
2 PhD student in Computer Engineering, Gorgan Azad University, Iran
Abstract
Water resources management in arid and semi-arid regions is one of the fundamental challenges of the present century. In this context, monthly runoff prediction serves as a strategic tool for reservoir planning, flood control, and sustainable watershed management. However, the complexity of hydrological processes and the nonlinear relationships among climatic variables make such predictions challenging. The present study aims to evaluate the performance of linear and deep learning models and to introduce an optimal model for enhancing water resources management.
Materials and methods:
In this study, monthly runoff in the Qarasu watershed was modeled and predicted using data from three hydrometric stations (Pole-Touskestan, Naharkhoran, and Siyahab) over a common 36-year period. Three single models (SARIMA, BiLSTM, and GRU) and two hybrid models (BiLSTM-GRU and SARIMA-BiLSTM-GRU) were employed to model monthly runoff and forecast values for a 12-month horizon. Model performance was evaluated using RMSE, MAD, and MSE indices.
Results and discussion:
The hybrid SARIMA-BiLSTM-GRU model, by integrating linear and nonlinear components, provided the most accurate monthly runoff predictions. The RMSE values of this model at the Pole-Touskestan, Naharkhoran, and Siyahab stations were estimated at 0.0295, 0.0173, and 0.1683 m³/s, respectively. The BiLSTM-GRU model ranked second, with RMSE values of 0.0326, 0.0226, and 0.3013 m³/s. Among the individual models, BiLSTM and GRU produced similar and relatively accurate results, while the linear SARIMA model, showed the lowest performance. On average, the SARIMA-BiLSTM-GRU hybrid model reduced prediction errors by 39.66% to 56.75% compared to the other models.
Conclusions:
This study demonstrated that hybrid models combining linear and deep learning approaches can significantly improve the accuracy and stability of monthly runoff predictions. Among them, the SARIMA-BiLSTM-GRU model provided the best performance by integrating both linear and nonlinear components.
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