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Assessment of water resources carrying capacity using chaotic particle swarm genetic algorithm

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2024年第2期60卷 667-686页

作者： Gao, Yuqin Gao, Li Liu, Yunping Wu, Ming Zhang, Zhenxing Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China Univ Illinois Prairie Res Inst Illinois State Water Survey Champaign IL USA

Water resources carrying capacity (WRCC) has been evaluated repeatedly to guide sustainable regional development, with the increasing conflicts over water resources between society and nature. Urban underlying surfaces are constantly changing under the rapid development of urbanization, which has changed the WRCC. The chaotic particle swarm genetic algorithm (CPSGA) is proposed in this study to evaluate the WRCC. It combines the genetic algorithm (GA), chaotic optimization algorithm (COA), and particle swarm optimization (PSO), as well as introduces the chaotic mapping of COA and the velocity position update strategy of PSO into the GA framework to strengthen the population quality and improve the algorithm's efficiency. The effectiveness of CPSGA was demonstrated using three typical functions. Nanjing, China, was used as the study area to evaluate the WRCC from 2015 to 2018. The results showed that the comprehensive evaluation scores of the WRCC of Nanjing from 2015 to 2018 were up to 0.83. In addition, the CPSGA had better astringency and stability than GA, COA, and PSO. The application indicated that the proposed methodology is feasible, providing a reference for conducting WRCC research elsewhere.

关键词： water resources carrying capacity chaotic particle swarm genetic algorithm evaluation model urbanization

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Application and effect evaluation of different optimization algorithms in distributed hydrological model

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2025年 58卷

作者： Wu, Ming Gao, Yuqin Liu, Yunping Xu, Longsheng Gao, Li Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China

Study Region: The upper reaches of the Shaying River Basin (the USR Basin) in the Huai River Basin, China Study Focus: The calibration of model parameters is one of the key challenges in advancing the application of hydrological models. The study proposes a novel metaheuristic optimization algorithm, the chaotic particle swarm genetic algorithm (CPSGA), and compares its performance with four well-known optimization algorithms in the field of hydrological model calibration: GA, PSO, DE, and SCE-UA. The comparison focuses on effectiveness, stability, time consumption, and convergence characteristics. New Hydrological Insights for the Region: During the parameter calibration process of runoff simulation in the USR Basin, CPSGA demonstrates strong effectiveness and convergence characteristics. It enhances the GA framework by integrating initial population chaotization, perturbation evolution, and sub-adaptation strategies, which improve individual diversity and facilitate targeted evolution, thereby increasing effectiveness and convergence. However, these enhancements compromise stability and increase time consumption compared to other algorithms. While PSO shows the best convergence characteristics, it suffers from reduced swarm diversity in later iterations, leading to local optima and poor effectiveness. The complex concept and competitive complex evolution (CCE) strategy of SCE-UA make it less effective for optimization problems with a considerable number of variables, limiting its suitability for calibrating fully distributed hydrological models. These results can provide reference for parameter calibration and uncertainty analysis in distributed hydrological models.

关键词： Optimization algorithm chaotic particle swarm genetic algorithm Distributed hydrological model Parameter calibration Performance evaluation

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