A multi-objective operation optimization method for dynamic control of reservoir water level in evolving flood season environments
•Dynamic reservoir water control model in evolving flood season environments is proposed.•A dynamic multi-objective multi-strategy co-evolution algorithm is proposed.•Simulation experiments validate the superior performance of the proposed model. Current multi-objective optimization methods, traditi...
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| Published in: | Journal of hydrology (Amsterdam) Vol. 643; p. 131940 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.11.2024
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| ISSN: | 0022-1694 |
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| Abstract | •Dynamic reservoir water control model in evolving flood season environments is proposed.•A dynamic multi-objective multi-strategy co-evolution algorithm is proposed.•Simulation experiments validate the superior performance of the proposed model.
Current multi-objective optimization methods, traditionally rooted in static models, often neglect uncertainties and environmental interactions such as forecast accuracy and reservoir conditions. This study introduces a novel multi-objective operational optimization model aimed at dynamically controlling reservoir water levels in evolving flood season environments. The proposed model conducts a comprehensive analysis, quantification, and prediction of water level control dynamics during flood seasons by integrating strategies that encompass runoff forecast acquisition, dynamic risk assessment, and adaptive decision-making responses. To enhance the model’s effectiveness, this research proposes the Dynamic Multi-Objective Multi-Strategy Co-evolution (DMMC) algorithm. This algorithm incorporates several strategies, including memory-based individual optimal adaptation, dynamic updating of diverse individuals, collaborative updating based on forecast data, and static optimization techniques. These strategies enable real-time monitoring, identification, and efficient response to environmental fluctuations, thereby optimizing the sustainable utilization of water resources. Numerical experiments and engineering case studies validate the efficacy of the proposed method, demonstrating its capability to accurately capture environmental trends and promptly respond to evolving conditions. The simulations confirm the rationality and reliability of the model, presenting a novel approach for effectively managing dynamic water level control during flood seasons. |
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| AbstractList | •Dynamic reservoir water control model in evolving flood season environments is proposed.•A dynamic multi-objective multi-strategy co-evolution algorithm is proposed.•Simulation experiments validate the superior performance of the proposed model.
Current multi-objective optimization methods, traditionally rooted in static models, often neglect uncertainties and environmental interactions such as forecast accuracy and reservoir conditions. This study introduces a novel multi-objective operational optimization model aimed at dynamically controlling reservoir water levels in evolving flood season environments. The proposed model conducts a comprehensive analysis, quantification, and prediction of water level control dynamics during flood seasons by integrating strategies that encompass runoff forecast acquisition, dynamic risk assessment, and adaptive decision-making responses. To enhance the model’s effectiveness, this research proposes the Dynamic Multi-Objective Multi-Strategy Co-evolution (DMMC) algorithm. This algorithm incorporates several strategies, including memory-based individual optimal adaptation, dynamic updating of diverse individuals, collaborative updating based on forecast data, and static optimization techniques. These strategies enable real-time monitoring, identification, and efficient response to environmental fluctuations, thereby optimizing the sustainable utilization of water resources. Numerical experiments and engineering case studies validate the efficacy of the proposed method, demonstrating its capability to accurately capture environmental trends and promptly respond to evolving conditions. The simulations confirm the rationality and reliability of the model, presenting a novel approach for effectively managing dynamic water level control during flood seasons. Current multi-objective optimization methods, traditionally rooted in static models, often neglect uncertainties and environmental interactions such as forecast accuracy and reservoir conditions. This study introduces a novel multi-objective operational optimization model aimed at dynamically controlling reservoir water levels in evolving flood season environments. The proposed model conducts a comprehensive analysis, quantification, and prediction of water level control dynamics during flood seasons by integrating strategies that encompass runoff forecast acquisition, dynamic risk assessment, and adaptive decision-making responses. To enhance the model’s effectiveness, this research proposes the Dynamic Multi-Objective Multi-Strategy Co-evolution (DMMC) algorithm. This algorithm incorporates several strategies, including memory-based individual optimal adaptation, dynamic updating of diverse individuals, collaborative updating based on forecast data, and static optimization techniques. These strategies enable real-time monitoring, identification, and efficient response to environmental fluctuations, thereby optimizing the sustainable utilization of water resources. Numerical experiments and engineering case studies validate the efficacy of the proposed method, demonstrating its capability to accurately capture environmental trends and promptly respond to evolving conditions. The simulations confirm the rationality and reliability of the model, presenting a novel approach for effectively managing dynamic water level control during flood seasons. |
| ArticleNumber | 131940 |
| Author | Wang, Yong-qiang Feng, Zhong-kai Niu, Wen-jing Yao, Xin-ru Mo, Li Zhang, Li |
| Author_xml | – sequence: 1 givenname: Li orcidid: 0009-0006-2620-0109 surname: Zhang fullname: Zhang, Li organization: The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China – sequence: 2 givenname: Zhong-kai surname: Feng fullname: Feng, Zhong-kai email: myfellow@163.com organization: The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China – sequence: 3 givenname: Xin-ru surname: Yao fullname: Yao, Xin-ru organization: The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China – sequence: 4 givenname: Wen-jing surname: Niu fullname: Niu, Wen-jing organization: Bureau of Hydrology, ChangJiang Water Resources Commission, Wuhan 430010, China – sequence: 5 givenname: Yong-qiang surname: Wang fullname: Wang, Yong-qiang organization: Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430010, China – sequence: 6 givenname: Li surname: Mo fullname: Mo, Li organization: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Keywords | Multi-objective optimization Flood control risk Reservoir operation Water level dynamic control |
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| Snippet | •Dynamic reservoir water control model in evolving flood season environments is proposed.•A dynamic multi-objective multi-strategy co-evolution algorithm is... Current multi-objective optimization methods, traditionally rooted in static models, often neglect uncertainties and environmental interactions such as... |
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| SubjectTerms | algorithms coevolution decision making Flood control risk Multi-objective optimization prediction Reservoir operation risk assessment runoff system optimization Water level dynamic control |
| Title | A multi-objective operation optimization method for dynamic control of reservoir water level in evolving flood season environments |
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