Document Type : Original Article

Authors

• Parisa-Sadat Ashofteh ¹
• Leila Shakarami ²

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

² Department of Civil Engineering, University of Qom, Qom, Iran

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

• Climate Change
• Long Short-Term Memory (LSTM)
• Horse Herd Optimization Algorithm (HHOA)
• Water Allocation Optimization
• Resiliency

Main Subjects

• Climate change