长江科学院院报 ›› 2016, Vol. 33 ›› Issue (9): 33-39.DOI: 10.11988/ckyyb.20150615

• 水资源与环境 • 上一篇    下一篇

组合权重模糊联系度模型在水质评价中的应用

叶章蕊a,b,卢毅敏a,b   

  1. 福州大学a.福建省空间信息工程研究中心;b.空间数据挖掘与信息共享教育部重点实验室,福州 350002
  • 收稿日期:2015-07-22 修回日期:2015-08-25 出版日期:2016-09-25 发布日期:2016-09-22
  • 作者简介:叶章蕊(1991-),女,福建宁德人,硕士研究生,主要从事水文水资源不确定性研究,(电话)13459041910(电子信箱)Yezhangrui1204@163.com。
  • 基金资助:
    福建省重点科技项目(2013Y0060);数字福建重点建设项目(闽发改网高技函〔2013〕84号)

Application of Fuzzy Connection Degree Model Based on Combined Weights to Evaluate Water Quality

YE Zhang-rui1,2,LU Yi-min1,2   

  1. 1.Spatial Information Research Center of Fujian Province, Fuzhou Universiy, Fuzhou 350002, China;
    2. Key Lab of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou 350002,China
  • Received:2015-07-22 Revised:2015-08-25 Published:2016-09-25 Online:2016-09-22

摘要: 针对水质评价指标存在的不确定性和水质评价标准存在的模糊性,基于集对分析理论与模糊层次分析法构建了模糊联系度水质评价模型。首先计算各评价指标值的分级联系度,对样本指标值做初步分类;再计算各评价样本与水质标准之间的综合联系度;最后通过置信度准则评判评价样本的水质级别。为突出不同评价指标的贡献率,将熵值赋权法和超标加权法引入该模型,并通过理想点法进行权重的合成,实现了多种赋权方法优势的融合。将模型应用于闽江渔业水域的水质评价,结果表明基于组合权重的模糊联系度水质评价结果更贴近实际情况,评价结果合理可信。

关键词: 集对分析, 分析指标分类, 模糊联系度, 组合权重, 水质评价

Abstract: In view of the uncertainty of evaluation indexes of water quality and the fuzziness of water quality standard, a fuzzy connection degree model of water quality evaluation was constructed based on set pair analysis and fuzzy analytical hierarchy process. First of all, the index values of water samples were preliminarily classified by calculating the hierarchical connection degree of each evaluation index value. Then the comprehensive degree of connection between samples and water quality standard was calculated. Finally, water quality grade was judged by confidence criterion. To highlight the contribution of different evaluation indexes, entropy method and super weighting method were introduced. Then the weights were combined based on ideal point method, by which the index weights were more reasonable. This model was applied to the evaluation of the fishery waters of Minjiang River, and the result was compared with those from gray classification method, synthesis index method and single factor evaluation method. The results obtained by the proposed model were closer to the real situation, and hence are reliable.

Key words: set pair analysis, classification of indexes, fuzzy connection degree, combination weight, water quality evaluation

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