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中文摘要：
洪水预报中影响因素很多,蕴含的信息也很复杂,如何从这些信息中获取有效信息是提高洪水预报精度的关键.考虑到洪水聚类时,指标间的相关性和信息冗余会严重影响分类效果,从而造成分类洪水预报精度不佳,应用主成分分析方法,力求提取历史洪水的有效信息,并以这些信息为基础,运用K均值聚类方法将历史洪水分类,对各类型洪水分别率定参数,通过计算洪水指标到各聚类中心的距离来判别即将发生洪水的归属类别,采用对应的模型参数进行预报.应用于实际流域,结果表明,基于主成分分析的分类洪水预报能够有效减小运算量,提高洪水预报精度.
英文摘要：
Flood forecasting is affected by so many factors that accessing to effective information makes the key to improve the flood forecasting accuracy.It is considered that there exits dependency among flood indexes which may affects the result of classification that is the key factor to forecasting accuracy;then the principal component analysis was applied to extract effective information from historical floods.The information was then used as the basis to classify the historical floods.After that,model parameter calibration,directed to different kinds of floods,was redone.Finally,the testing floods were forecasted by using the parameter that fitted them best.It turned out that the computational cost was reduced and the forecasting accuracy was improved by using the method.
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