2010 , Vol. 30 >Issue 6: 916 - 920

• 1. 合肥工业大学土木与水利工程学院, 安徽 合肥 230009;
2. 北京理工大学管理与经济学院, 北京 100081;
3. 南京水利科学研究院水文水资源与水利工程科学国家重点实验室, 江苏 南京 210029

修回日期: 2010-11-15

网络出版日期: 2010-11-20

Application of Accelerating Genetic Algorithm to Parameter Estimation of Muskingum Flood Routing Model

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• 1. School of Civil Engineering, Hefei University of Technology, Hefei, Anhui 230009, China;
2. School of Management and Economic, Beijing Institute of Technology, China, Beijing 100081, China;
3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, Jiangsu 210029, China

Revised date: 2010-11-15

Online published: 2010-11-20

### Abstract

River flood routing is very important in regional flood disaster management. Now Muskingum flood routing model has widely been applied in river flood routing because of its simple and convenient computation and well applicability. In order to improve accurateness, stability and efficiency of the parameter estimation of Muskingum flood routing model and to facilitate flood forecasting, reservoir flood control operation and flood control planning, the parameter estimation of Muskingum flood routing model was transformed into a nonlinear optimal procession based on the fundamental hypothesis of Muskingum flood routing model in this paper. And an improved genetic algorithm, named accelerating genetic algorithm (AGA) was developed to optimize all of the model parameters of Muskingum flood routing model at the same time. The applied results show that AGA is more effective and high precision for the river flood routing compared with common parameter estimation methods such as try-and-error method, hunting method, and least square method. Due to its capability of realizing the optimization and simplification of the parameter estimation of Muskingum flood routing model, AGA can be widely applied to different complex optimal problems of flood disaster management.

### 参考文献

[1] 叶守泽.水文水利计算[M].北京:水利电力出版社,1992.
[2] 鲁 帆,蒋云钟,王 浩,等.多智能体遗传算法用于马斯京根模型参数估计[J].水利学报,2007,38(3):289~294.
[3] 雒文生,宋星原.洪水预报与调度[M].武汉:湖北科学技术出版社,2000.
[4] 魏一鸣,金菊良,杨存建,等.洪水灾害风险管理理论[M].北京:科学出版社,2002.
[5] 翟国静.马斯京根流量演进系数的直接优选法[J].河北工程技术高等专科学校学报,1996,(2):6~11.
[6] 杨荣富.马斯京根模型最优参数估计探讨[J].水文,1988,(4):18~21
[7] 张洪国,王福刚,耿冬青.马斯京根方程参数确定及其程序化实现[J].世界地质,2000,19(4):361~365.
[8] 杨晓华,金菊良,陈肇升,等.马斯京根模型参数估计的新方法[J].灾害学,1998,13(3):1~6.
[9] 黄国如,芮孝芳.河道洪水演算的径向基函数神经网络模型[J].河海大学学报(自然科学版),2003,31(6):621~625.
[10] 詹士昌.马斯京根洪水演算模型的改进——兼论其参数的蚁群算法率定[J].自然灾害学报,2006,15(2):32~37.
[11] 陈异植,庄希澄.马斯京根法评述[J].海河水利,1990,(5):1~6.
[12] 张 明,金菊良,张礼兵.流域可持续评价的最大熵原理——投影寻踪耦合模型[J].地理科学,2007,27(2):177~180.
[13] 金菊良,魏一鸣.复杂系统广义智能评价方法与应用[M].北京:科学出版社,2008.
[14] 金菊良,魏一鸣,丁 晶.投影寻踪门限自回归模型在年径流预测中的应用[J].地理科学,2002,22(2):171~175.
[15] 付 强,付 红,王立坤.基于加速遗传算法的投影寻踪模型在水质评价中的应用研究[J].地理科学,2003,23(2):236~239.
[16] 金菊良,张礼兵,魏一鸣. 基于遗传算法的理想区间法在洪水灾情评价中的应用[J].地理科学,2004,24(5):586~590.
[17] 金菊良,魏一鸣,丁 晶.基于遗传算法的水文时间序列变点分析方法[J].地理科学,2005,25(6):720~723.
[18] 林三益.水文预报(第二版)[M].北京:中国水利水电出版社,2001.
[19] 陈 莹,许有鹏,尹义星.土地利用/覆被变化下的暴雨径流过程模拟分析——以太湖上游西苕溪流域为例[J].地理科学,2009,29(1):117~127.
[20] 李谢辉,王 磊,谭灵芝,等.渭河下游河流沿线区域洪水灾害风险评价[J].地理科学,2009,29(5):733~739.
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