| 一种基于三维指标因子的流域多年径流丰枯k-means聚类法 |
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| 引用本文： | 庄承彬,陈晓宏,黄薇颖,彭涛.一种基于三维指标因子的流域多年径流丰枯k-means聚类法[J].生态环境,2010,26(6):1354-1357. |
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| 作者姓名： | 庄承彬 陈晓宏 黄薇颖 彭涛 |
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| 作者单位： | 中山大学水资源与环境系,广东,广州,510275;华南地区水循环和水安全广东普通高校重点实验室(中山大学),广东,广州,510275 |
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| 基金项目： | 国家自然科学基金重点项目，国家重点基础研究发展计划(973计划)专题，国家自然科学基金青年科学基金资助项目，教育部人文社会科学研究青年基金项目 |
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| 摘 要： | 径流丰枯聚类研究的传统方法多建立在年径流量的单一指标之上,容易导致分析的片面化。针对这个问题,提出了衡量流域多年径流丰枯状态的三维指标因子及权重,将其耦合到k-means聚类法的相似度计算与收敛分析中,在此基础上对对多年径流进行丰枯聚类,构建了一种基于三维指标因子的流域多年径流丰枯k-means聚类法。以该方法对广东省鉴江流域下游化州站1956—2006年的径流系列进行聚类分析,并与基于年径流量单一指标的k-means聚类方法进行对比,结果表明该方法是较全面且符合实际的。
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| 关 键 词： | 三维指标因子 k-means 丰枯 聚类 多年径流 广东省鉴江流域 |
A k-means method partitioning runoff into abundant and low state based on three-dimensional index factors |
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| ZHUANG Chenbin,CHEN Xiaohong,HUANG Weiying,PENG Tao.A k-means method partitioning runoff into abundant and low state based on three-dimensional index factors[J].Ecology and Environmnet,2010,26(6):1354-1357. |
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| Authors: | ZHUANG Chenbin CHEN Xiaohong HUANG Weiying PENG Tao |
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| Institution: | 1, 2 1. Department of Water Resources and Environment, SUN Yat-sen University, Guangzhou 510275, China 2. Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong Higher Education Institutes, SUN Yat-sen University, Guangzhou 510275, China |
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| Abstract: | The traditional cluster research method of runoff based on mere index of annual runoff, in this case, it is difficult to make comprehensive analysis. Intend to deal with this problem, this paper promotes three-dimensional factors and its weight to partition runoff into abundant and low state, and coupled it with similarity computation and convergence analysis of k-means. On this basis, this essay promotes a k-means method partitioning runoff into abundant and low state based on three-dimensional index factors. Use this method to analysis 1956-2006’s runoff series of Huazhou hydrologic station which located in downstream of Jianjiang River of Guangdong province, then compared with the method which using mere index of annual off, it turns out that the former is accords with real. |
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| Keywords: | k-means |
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