基于联系云的地下水水质可拓评价模型
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摘要
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.
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 0.01 confidence 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.
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 0.01 confidence 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.
引文
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[2]兰双双,姜纪沂,王滨.基于物元可拓法的地下水水质评价——以梨树县平原区浅层地下水为例[J].吉林大学学报(地球科学版),2009,39(4):722-727.
[3]崔嘉宇,张宁红,郁建桥,等.改进的模糊评价法在太湖水质评价中的应用[J].环境工程学报,2015,9(11):5357-5363.
[4]方运海,郑西来,彭辉,等.基于模糊综合与可变模糊集耦合的地下水质量评价[J].环境科学学报,2018,38(2):546-552.
[5]倪深海,白玉慧.BP神经网络模型在地下水水质评价中的应用[J].系统工程理论与实践,2000,20(8):124-127.
[6]刘姝媛,王红旗.某地下水水源地污染风险评价指标体系研究[J].中国环境科学,2016,36(10):3166-3174.
[7]巩奕成,张永祥,任仲宇,等.基于投影寻踪和模糊物元组合模型的地下水水质评价[J].北京工业大学学报,2014,40(9):1042-1047.
[8]邵磊,周孝德,杨方廷,等.基于自由搜索的投影寻踪水质综合评价方法[J].中国环境科学,2010,30(12):1708-1714.
[9]李久辉,卢文喜,常振波,等.基于不确定性分析的地下水污染超标风险预警[J].中国环境科学,2017,37(6):2270-2277.
[10]张质明,王晓燕,潘润泽.一种改进的不确定性水质模型参数率定方法[J].中国环境科学,2017,37(3):956-962.
[11]樊庆锌,杨先兴,邱微.松花江哈尔滨段城市水环境质量评价[J].中国环境科学,2014,34(9):2292-2298.
[12]周学松.模糊集的取大取小算法的不合理性[J].数学的实践与认识,2005,35(11):209-212.
[13]康志强,周辉,冯夏庭,等.大型岩质边坡岩体质量的可拓学理论评价[J].东北大学学报(自然科学版),2007,28(12):1770-1774.
[14]LI Deyi,HAN Jiawei,SHI Xuemei,et al.Knowledge representation and discovery based on linguistic atoms[J].Knowledge-Based Systems,1998,10(7):431-440.
[15]汪明武,金菊良.联系数理论与应用[M].北京:科学出版社,2017.
[16]汪明武,朱其坤,赵奎元,等.基于有限区间联系云的围岩稳定性评价模型[J].岩土力学,2016,37(s1):140-144.
[17]刘玲,陈娟娟,徐代忠.基于可拓云理论的施工安全综合评价[J].土木工程与管理学报,2017,34(3):39-44.