| 水文模型参数多目标率定及最优非劣解优选 |
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| 引用本文： | 周建中,卢韦伟,孙娜,叶磊,张海荣,陈璐.水文模型参数多目标率定及最优非劣解优选[J].水文,2017,37(2):1-7. |
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| 作者姓名： | 周建中 卢韦伟 孙娜 叶磊 张海荣 陈璐 |
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| 作者单位： | 1.华中科技大学水电与数字化工程学院2.大连理工大学水利工程学院 |
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| 基金项目： | 国家自然科学基金重大研究计划重点支持项目(91547208);国家自然科学基金项目(51579017)；水利部公益性行业科研专项经费项目(201401014-2)； |
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| 摘 要： | 针对概念性水文模型参数众多、相互制约,且多目标参数优化率定最优参数求解困难、易受决策者主观因素影响的问题,采用多目标优化算法对水文模型参数进行率定,得到模型参数最优非劣解集,在此基础上,引入最小最大后悔值决策理论,并结合Pareto支配基本理论,提出了一种多目标最优非劣解选取准则。以柘溪流域为研究对象,采用三目标MOSCDE优化率定新安江模型的参数,并与单目标SCE-UA优化结果进行对比分析。结果表明,提出的非劣解选取方法可以有效从大规模非劣解集中筛选出最优非劣解,大大缩短参数率定耗时。
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| 关 键 词： | 水文模型参数率定 多目标优化 参数最优非劣解优选 最小最大后悔值决策 |
| 收稿时间： | 2016/7/9 0:00:00 |
Study on Multi-objective Calibration of Hydrological Model and Optimization Method of Optimal Pareto Solutions |
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| ZHOU Jianzhong,LU Weiwei,SUN N,YE Lei,ZHANG Hairong,CHEN Lu.Study on Multi-objective Calibration of Hydrological Model and Optimization Method of Optimal Pareto Solutions[J].Hydrology,2017,37(2):1-7. |
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| Authors: | ZHOU Jianzhong LU Weiwei SUN N YE Lei ZHANG Hairong CHEN Lu |
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| Institution: | 1. College of Hydropower & Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, China; 2. School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China |
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| Abstract: | Numerous parameters of conceptual hydrological model have an inter-constraint relationship between each other and it is difficult to choose the optimal parameters of multi-objective parameter optimization due to the influences of subjective factors of policy makers. To solve this problem, we adopted a multi-objective optimization algorithm to calibrate hydrologic model parameters and obtained a series of Pareto optimal sets of model parameters. Based on these sets, we introduced the minimum maximum regret decision theory and combined the basic theory of Pareto dominance. Then we put forward a multi-objective optimal selection principles of Pareto solutions. Taking the Zhexi watershed as the research object, we used MOSCDE to calibrate the parameters of hydrological model and compared the results with the single objective optimization results. The results indicated that the proposed method can effectively select the optimal solution and is not limited by the large scale of Pareto optimal sets. In addition, this method can also greatly reduce the amount of time. |
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| Keywords: | parameter calibration of hydrological model multi-objective optimization selection of optimal parameters minimum and maximum regret decision theory |
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