A Hybrid GA-PSO Algorithm for Optimal Reservoir Operation Fulltext
[ Hamid Bashiri Atrabi ] - MSc Student of Water Resources Engineering, Department of Water Engineering, Young Researchers Association of Shahid Bahonar University of Kerman
[ Kourosh Qaderi ] - Assistant Prof., Department of Water Engineering, Shahid Bahonar University of Kerman
[ Shaharam Karimi ] - Assistant Prof., Department of Water Engineering, Shahid Bahonar University of Kerman
[ Erfaneh Sharifi ] - M.Sc. Student of Water Resources Engineering, Department of Water Engineering, Young Researchers Association of Shahid Bahonar University of Kerman
In spite of centuries of experience with flood management, floods still cause victims and economic damages. The northern region of Iran is endowed with rich water resources but their mismanagement and continuous human interference has rendered them in a fragile state. Many approaches are available for the operation of reservoir during the flood month, one of them being separate allocation of storage space for flood control. However, to keep the reservoir level at the minimum possible, a number of multipurpose projects are constructed without sufficient exclusive flood storage,thereby necessitating optimum and judicious management of reservoirs during the flood month.This paper presents an evolutionary algorithm based on the hybrid genetic algorithm (GA) and particle swarm optimization (PSO), denoted by HGAPSO. This algorithm isdeveloped in order to optimal reservoir operation in north of Iran. The optimization puts focus on the trade-off between flood control and irrigation demands for the Narmab reservoir operation in the flood month. The results demonstrate an optimized rule can be found for both reduces downstream flood peaks and maintains a high reservoir level for irrigation demands in the flood month. This study also demonstrates the usefulness of HGAPSO for water resource management problems.
Reservoir Operation, HGAPSO, Optimization, Flood Control, Narmab
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