[1]胡彩虹,王艳菊,吴泽宁..基于聚类的支持向量机在洪水预报中的应用[J].郑州大学学报(工学版),2009,30(04):123-127.
　HU Caihong,WANG Yanju,WU Zening.Application of Support Vector Machine Based on Clusteringin Flood Foreeast[J].Journal of Zhengzhou University (Engineering Science),2009,30(04):123-127.
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基于聚类的支持向量机在洪水预报中的应用()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
30
期数:
2009年04期
页码:
123-127
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Application of Support Vector Machine Based on Clusteringin Flood Foreeast
作者:
胡彩虹; 王艳菊; 吴泽宁.
郑州大学水利与环境学院,河南郑州,50001, 郑州大学水利与环境学院,河南郑州,50001, 郑州大学水利与环境学院,河南郑州,50001
Author(s):
HU Caihong; WANG Yanju; WU Zening
School of Water Conservancy and Environment Engineering，Zhengzhou University，Zhengzhou 450001，China
关键词:
系统聚类;  支持向量机;  洪水预报
Keywords:
system clustering; support vector machine; flood forecasting
文献标志码:
A
摘要:
半干旱地区的特殊特点使其径流模拟计算难度增大,且难以获得较详细的资料,因而洪水预报难度大,尤其是洪峰流量的预报.若应用所有样本进行模型参数确定并预报,不能完全反映洪水的不同特性.因此采用了基于聚类分析的支持向量机模型,以半干旱半湿润地区的岚河流域为例,进行了模拟检验,结果表明,效率系数大部分达到85%以上,平均相对误差绝对值多数都小于1.5%.另外洪峰流量相对误差绝对值均在15%以内,特别洪峰流量较大的几场洪水,相对误差小于1%.洪峰流量和峰现时差合格率均达100%.
Abstract:
The difficulty of the runoff simulation has been increased due to the specificity of the semi—arid re．gion，which is still a hot and difficult topic in current study．The detailed information is hard to obtain becauseof the complicated process of the runoff，SO it is difficult to forecast flood，especially the flood peak forecast．The different characteristics of flood can not be reflected completely if all samples were used to calibrate theparameter of the model．Thus the support vector machine method based on clustering is used．And the LanRiver basin in the seni—arid region is taken as an example to be simulated and tested．The results haveshown that most efficient eoemcient iS beyond 85％．and the modulus of the relatively average error is mostlyless than 1．5％．Additionally，the peak flow modulus of the relatively average error is less than 1 5％。particu．1arly in the flood of large peak flow．the relatively average error is less than 1％．The qualification rate of thepeak flow and the peak time difference is 1 00％．

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更新日期/Last Update: 1900-01-01