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首页> 外文期刊>Journal of Hydrology >Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration
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Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration

机译：模糊最优模型与遗传算法相结合求解多目标降雨径流模型标定

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摘要

An automatic calibration methodology for the Xinanjiang model that has been successfully and widely applied in China is presented. The automatic calibration of the model consists of two parts: water balance parameter and runoff routing parameter calibration. The former is based on a simple genetic algorithm (GA), The latter is based on a new method which combines a fuzzy optimal model (FOM) with a GA for solving the multiple objective runoff routing parameters calibration problem, Except for the specific fitness where the membership degree of alternative obtained by FOM with limited alternatives and multiobjectives is employed. the GA with multiple objectives in this paper is otherwise the same as the simple GA. The parameter calibration includes optimization of multiple objectives: (1) peak discharge. (2) peak time and (3) total runoff volume. Thirty-four historical floods from 12 years in the Shuangpai Reservoir are applied to calibrate the model parameters whilst I I floods in recent 2 years are utilized to verify these parameters. Results of this study and application show that the hybrid methodology of GAs and the FOM is not only capable of exploiting more the important characteristics of floods but also efficient and robust. (C) 2002 Elsevier Science B.V. All rights reserved. [References: 26]
机译：提出了在中国已经成功并广泛应用的新安江模型自动校准方法。模型的自动标定由两部分组成：水平衡参数和径流径流参数标定。前者基于简单遗传算法（GA），后者基于将模糊最优模型（FOM）与遗传算法相结合的新方法，用于解决多目标径流路由参数校准问题，但具体适用性除外采用了由FOM获得的具有有限选择和多目标的选择的隶属度。本文中具有多个目标的GA与简单GA相同。参数校准包括多个目标的优化：（1）峰值放电。 （2）高峰时间和（3）总径流量。利用双牌水库12年的34次历史洪水来标定模型参数，而最近2年的I I洪水被用来验证这些参数。这项研究和应用的结果表明，GA和FOM的混合方法不仅能够充分利用洪水的重要特征，而且具有高效和鲁棒性。 （C）2002 Elsevier Science B.V.保留所有权利。 [参考：26]

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