| 有序聚类分析法的改进及其在水文序列突变点识别中的应用 |
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| 引用本文： | 袁满,王文圣,叶濒璘.有序聚类分析法的改进及其在水文序列突变点识别中的应用[J].水文,2017,37(5):8-11. |
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| 作者姓名： | 袁满 王文圣 叶濒璘 |
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| 作者单位： | 四川大学水利水电学院四川大学水利水电学院 |
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| 基金项目： | 国家自然科学基金项目(51679155)； |
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| 摘 要： | 有序聚类分析法是水文学中识别突变点的有效方法,但该法只考虑了同类之间的离差较小原则,忽略了类与类之间的离差较大原则。基于此,提出了改进的有序聚类分析法,改进法同时考虑了同类之间的离差较小和类类间的离差较大原则。将改进的有序聚类分析法应用于年平均流量序列突变点识别中,并与传统有序聚类分析法进行对比分析,研究结果表明,改进的有序聚类分析法原理明确,识别突变点更加有效。
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| 关 键 词： | 突变点识别 改进的有序聚类分析法 水文序列 显著性检验 |
| 收稿时间： | 2016/9/23 0:00:00 |
Improvement of Sequential Clustering Method and Its Application to Diagnose Jump Points of Hydrological Series |
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| YUAN Man,WANG Wensheng,YE Binlin.Improvement of Sequential Clustering Method and Its Application to Diagnose Jump Points of Hydrological Series[J].Hydrology,2017,37(5):8-11. |
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| Authors: | YUAN Man WANG Wensheng YE Binlin |
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| Institution: | College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China |
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| Abstract: | The sequential clustering method is an effective way to diagnosejump points in hydrologic research. However, the method only considers smaller deviations between the same classes, but ignores the larger deviations between different classes. For that reason, this paper proposedthe improved sequential clusteringmethod considering both smaller deviations between same classes and larger deviations between different classes. The improved sequential clustering methodhas been applied to identify jump points ofsome average annual flow seriesand comparedwith traditional sequential clustering method. The results show that theimproved sequential clusteringmethod is clear and effective to identify jump points. |
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| Keywords: | diagnosis of jump points improved sequential clustering method hydrological series significance test |
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