基于联系云的地下水水质可拓评价模型

地下水水质评价受诸多不确定因素的影响，评价指标具有模糊性、随机性和离散性特征.为真实反映指标的分布特征，提高地下水水质评价的合理性与可靠性，在此将可拓学与联系云理论耦合，提出能够描述评价指标在分类等级间转换态势的联系云可拓模型，即基于指标分类标准确定联系云数字特征，生成有限区间内的联系云与构建联系云可拓矩阵，实现统一定量描述地下水水质评价指标的确定和不确定性，然后结合权重，分析待评价物元与地下水水质等级的联系，综合确定待评物元的水质等级，并给出评价结果的可信程度.实例应用及与投影寻踪和模糊物元耦合方法结果的对比表明，本文模型评价结果与投影寻踪模糊物元方法的结果基本吻合，且基于联系云可拓模型评价地下水水质等级置信因子均小于0.01，更有效可行，并克服了可拓方法不能反映评价指标模糊性特征的缺陷.

A novel extension evaluation model of groundwater quality based on connection cloud model

The evaluation of quality grade of groundwater involving various uncertainty factors is of fuzzy, random and discrete characteristics. In order to reflect distribution characteristics of evaluation indexes and improve the rationality and reliability of groundwater quality classification, a connection cloud model coupled with extension theory was proposed here to describe conversion situation in the classification ranks. Firstly, digital characteristics of connection cloud model based on the classification standard were identified, and the connection cloud mapping in the finite intervals was generated to simulate the classification standard. Namely, certainty and uncertainty relationships between the measured evaluation indicators and their quality grades might be depicted by a connection cloud from a unified perspective. Then combined with index weight, the extension matrix based on the connection cloud was constructed to analyze the relationship between measured evaluation indicators and quality grades. Next, quality grade was specified by the comprehensive cloud correlation degree, credible degrees of the evaluation results were also given. Finally, case studies and comparison with the projection pursuit based on fuzzy matter-element method were conducted, the results with less than 0.01confidence factor obtained by the model proposed here do well agreement with those by the projection pursuit method, and are more feasible and effective. Moreover, it can overcome the shortcomings of the extension theory that cannot reflect the fuzzy characteristic of the evaluation index.