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基于模糊综合与可变模糊集耦合的地下水质量评价 被引量:27

Groundwater quality evaluation based on fuzzy synthetic evaluation and variable fuzzy sets
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摘要 模糊综合评价法和可变模糊集方法是常用的地下水质量评价方法,但可变模糊集评价法的隶属度计算复杂、可变参数多,而模糊综合评价法容易造成隶属度信息丢失.本文在充分吸取两者优点的基础上,构建了一种新的耦合地下水质量评价模型,优化了评价因子选择、权重确定、模糊算子选择等关键环节,并对青岛市大沽河水源地的地下水质量进行了评价和分析对比.结果表明,水源地中北部(W7~W11)地区,硝酸盐氮含量严重超标,耦合模型得到的水质等级均为4或5类水,评价结果符合该区域实际水质状况,说明新建的耦合模型是可靠的.另外,从监测井W2、W3、W4、W7、W10、W11、W12获得的最大隶属度小于0.5,说明模糊综合评价法不适用于这些点的水质评价,而耦合模型综合考虑了其它隶属度值对评价结果的贡献,其结果更加科学、合理. The fuzzy synthetic evaluation and the variable fuzzy sets are common methodsin groundwater quality evaluation. However, the membership calculation of the variable fuzzy sets is complicated and accompanied by many variable parameters. And the fuzzy synthetic evaluation may lose membership information. Combining with the advantages of these two methods, this paper establishes a new coupling model to evaluate groundwater quality.This model has several crucial improvements in selecting evaluation indices, calculating weighted values and choosing fuzzy synthetic operators. We applied this model in a case of groundwater quality evaluation for a groundwater source field in the Dagu River Basin of Qingdao and compared with other methods. The results indicate that the nitrate content of the mid-north areas (W7 - Wll ) exceeded the criterion seriously, with the evaluation level of water quality in the coupling model at 4 or 5. Therefore the evaluation results are consistent with the actual status, which demonstrates that the coupling model is reliable. Moreover, the largest membership of monitoring well W2, W3, W4, W7, W10, Wll, and W12 are less than 0.5 in the fuzzy synthetic evaluation, illustrating that the fuzzy synthetic evaluation is not suitable for these wells. The coupling model takes into account other memberships, which makes its results more scientific and reasonable.
出处 《环境科学学报》 CAS CSCD 北大核心 2018年第2期546-552,共7页 Acta Scientiae Circumstantiae
基金 国家重点研发计划项目(No.2016YFC0402810) 国家自然科学基金(No.51409236)~~
关键词 地下水 水质评价 模糊综合评价 可变模糊集 groundwater water quality evaluation fuzzy synthetic evaluation variable fuzzy sets
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