模糊模式识别模型在古交矿区汾河支流水质评价中的应用  

Application of fuzzy pattern recognition model in water quality assessment of Fenhe River tributary at Gujiao mining section

作　　者：陈姣艳[1] 李洪建[1] 严俊霞[1] 杨永刚[1] 

机构地区：[1]山西大学黄土高原研究所,山西太原030006

出　　处：《水资源与水工程学报》2016年第1期96-100,共5页Journal of Water Resources and Water Engineering

基　　金：山西省科技厅软科学项目(2014041016-1);国家国际科技合作专项项目(2012DFA20770)

摘　　要：汾河古交段是太原市地下水源地的主要补给区,污染比较严重,而其支流是污染源之一。本研究采用相关系数法确定指标权重,应用模糊模式识别模型对该段主要支流水质进行了综合评价。结果表明：该段汾河9条支流地表水质的级别特征值均在1-2之间,水质较好。大川河、狮子河、天池川3条支流的水质类别近似为Ⅰ类,其它6条支流的水质类别近似为Ⅱ类。该评价结果比较合理和客观,为汾河古交段水资源的污染控制和综合治理提供理论依据。Gujiao mining section in Fenhe River is a main recharge area of groundwater source of Taiyuan,and the pollution in the section is more serious. The tributaries in the section is one of several pollution sources. The paper applied the fuzzy pattern recognition model with the method of correlation coefficient to determine the index weights so as to assess the water quality of the tributaries of Gujiao mining section in Fenhe River comprehensively. The results showed that the level characteristic value of water quality of nine tributaries is between 1 and 2,and the water quality of the section is better. The water quality level in Dachuan,Shizi and Tianchi river is approximate level Ⅰ,and that of other 6 tributaries is level Ⅱ. The evaluation result is reasonable and objective,which can provide academic reference for the pollution control and comprehensive management of water resources in Gujiao section of Fenhe river.

关 键 词：水质评价 模糊识别模型 古交矿区 汾河支流 

分 类 号：X824[环境科学与工程—环境工程]