Modeling and Optimization of Water Allocation Using a Hybrid LSTM–HHOA Approach Under Climate Change Conditions

Ashofteh, Parisa-Sadat; Shakarami, Leila

doi:10.22077/jdcr.2025.9958.1165

Document Type : Original Article

Authors

¹ Associate Professor, Department of Civil Engineering, University of Qom, Qom, Iran

² Ph.D. Student, Department of Civil Engineering, University of Qom, Qom, Iran

https://doi.org/10.22077/jdcr.2025.9958.1165

Abstract

The present study aimed to optimize water allocation from the Qarnaku Dam for the future period (2070–2099) and enhance the resilience of the water supply system. Among CMIP5 climate models, GFDL-ESM2G was selected as the most suitable option for temperature, and NorESM1-M was selected for precipitation. Subsequently, monthly runoff was simulated using Long Short-Term Memory (LSTM) networks, yielding satisfactory accuracy. Agricultural water demand was estimated using the Cropwat model, indicating a 20% increase in future net irrigation requirements. The performance of the Horse Herd Optimization Algorithm (HHOA) and Genetic Programming (GP) was evaluated using the Rastrigin and Rosenbrock benchmark functions, revealing superior results for HHOA. Further analysis showed that HHOA reduced water deficits by up to 22% and improved reservoir resilience from 67% to 79%. Additionally, the LSTM model achieved a Nash–Sutcliffe Efficiency (NSE) coefficient of 0.85, confirming its reliability in simulating monthly runoff. Overall, the integration of advanced metaheuristic techniques with intelligent runoff prediction models provides an effective framework for reservoir management under climate change conditions.

Keywords

Main Subjects

Climate change

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Journal of Drought and Climate change Research

Modeling and Optimization of Water Allocation Using a Hybrid LSTM–HHOA Approach Under Climate Change Conditions

References

References

Volume 4, Issue 1 - Serial Number 13
May 2026
Pages 131-158

Modeling and Optimization of Water Allocation Using a Hybrid LSTM–HHOA Approach Under Climate Change Conditions

References

References

Volume 4, Issue 1 - Serial Number 13May 2026Pages 131-158

Volume 4, Issue 1 - Serial Number 13
May 2026
Pages 131-158