资源与生态学报 ›› 2010, Vol. 1 ›› Issue (3): 249-252.DOI: 10.3969/j.issn.1674-764x.2010.03.008

• 水专题 • 上一篇    下一篇

主成分分析和熵值法相结合的水质综合评价模型及其在郑州市金水河的应用

马建琴1,郭晶晶1, 刘晓洁2   

  1. 1 华北水利水电学院,郑州 450011;
    2 中国科学院地理科学与资源研究所,北京 10010
  • 收稿日期:2010-08-16 修回日期:2010-09-07 出版日期:2010-11-10 发布日期:2010-11-04

Water Quality Evaluation Model Based on Principal Component Analysis and Information Entropy: Application in Jinshui River

MA Jianqin1*, GUO Jingjing1 and LIU Xiaojie2   

  1. 1 North China University of Water Conservancy and Hydroelectric Power, Zhengzhou 450011, China;
    2 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2010-08-16 Revised:2010-09-07 Online:2010-11-10 Published:2010-11-04
  • Contact: MA Jianqin

摘要: 水质评价对决策者决定水的使用功效尤为重要。水质综合评价系统中涉及到大量因子与指标,因子之间相互作用,致使水质的评价工作相对困难。主成分分析法可以消除因子间的相关性,因而被广泛应用于水质评价,但其忽略了数据离散程度的问题。熵值法则考虑了数据的离散特点。为更好地进行水质的综合评价,本文提出把主成分分析法和熵值法结合起来确定指标权重的方法,建立了水质评价模型,并采用该模型对郑州市金水河再生水2009年的水质情况进行评价,将评价结果与单独采用主成分分析或熵值法的结果进行了比较。结果表明了该方法的可行性与实用性,能够为非常规水资源利用提供理论依据和决策参考。

关键词: 影响因子;, 质评价;, 成分分析;, 值法;, 重;, 常规水

Abstract: Water quality evaluation is important because it could provide guidance when determining water utility. But many interacting impact factors are involved in water quality evaluation systems, making water quality evaluation difficult. Principal component analysis (PCA) is widely used in water quality evaluation because it can eliminate the correlation among factors. However, PCA ignores the degree of data dispersion, which is considered by information entropy (IE). To solve this problem, a model combined PCA and IE methods to obtain the weights of indicators is proposed in this paper, and the proposed model was applied to assess the reused water quality of Jinshui River in Zhengzhou City in 2009. The evaluation results were compared with those using PCA and IE methods for the same data. The results proved that the method is feasible and practical, and it can provide a theoretical basis and decision reference for the utility of unconventional water.

Key words: impact factors, ater quality evaluation, rincipal component analysis (PCA), nformation entropy (IE), weight, nconventional water