基于主成分分析赋权的集对模型在水质评价中的应用
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摘要
准确快速的水质评价方法可以为区域水资源的开发、水环境的保护提供指导。以宁夏黄河干流段水体为研究对象,针对水质评价体系中各项指标不确定信息共存的特点,尝试采用集对分析法的确定和不确定分析对水质进行评价。首先初步判断样本和指标间的联系度,然后对样本各项指标做进一步的同、异、反分析,结合主成分分析法确定指标的权重,得出综合联系度值,最后由置信度准则确定水质等级,并将评价结果与其他评价方法的结果对比分析。研究发现,对比结果高度一致。因此,基于主成分分析赋权的集对模型可以更好地反映水质状况,可为黄河水资源的开发、水环境的保护和治理提供参考和借鉴。
Accurate and rapid water quality assessment method can provide guidance for the development of regional water resources and the protection of water environment. Taking Ningxia Yellow River as the research object,aiming at the coexistence of uncertain information of each index in the water quality evaluation system,this paper tries to use set pair analysis to evaluate the water quality. Firstly,the connection degree between the sample and the index is judged preliminarily,and then further identical,different and inverse analysis is made on each index of the sample. Combined with the weight of the index determined by the principal component analysis method,the comprehensive connection degree value is obtained. Finally,the water quality grade is determined by the confidence criterion,and the evaluation results are compared with those of other evaluation methods. The findings are highly consistent. Therefore,the centralized pair model based on principal component analysis and weighting can better reflect the water quality,and then provide reference and help for the development of the Yellow River water resources,water environment protection and management.
Accurate and rapid water quality assessment method can provide guidance for the development of regional water resources and the protection of water environment. Taking Ningxia Yellow River as the research object,aiming at the coexistence of uncertain information of each index in the water quality evaluation system,this paper tries to use set pair analysis to evaluate the water quality. Firstly,the connection degree between the sample and the index is judged preliminarily,and then further identical,different and inverse analysis is made on each index of the sample. Combined with the weight of the index determined by the principal component analysis method,the comprehensive connection degree value is obtained. Finally,the water quality grade is determined by the confidence criterion,and the evaluation results are compared with those of other evaluation methods. The findings are highly consistent. Therefore,the centralized pair model based on principal component analysis and weighting can better reflect the water quality,and then provide reference and help for the development of the Yellow River water resources,water environment protection and management.
引文
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[7]颜惠琴,牛万红,韩惠丽.基于主成分分析构建指标权重的客观赋权法[J].济南大学学报:自然科学版,2017,31(6):519-523.
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[9]莫崇勋,莫桂燕,阮俞理,等.五元集对分析在澄碧河流域径流分类中的运用[J].广西大学学报:自然科学版,2017,42(1):379-385.
[10]葛康,汪明武,陈光怡.基于集对分析与三角模糊数耦合的土壤重金属污染评价模型[J].土壤,2011,43(2):216-220.
[11]孙占超.基于云模型的水环境质量评价研究[D].西安:长安大学,2017.
[2]钱程,穆文平,王康,等.基于主成分分析的地下水水质模糊综合评价[J].水电能源科学,2016,34(11):31-35.
[3]尹炜,辛小康,梁建奎,等.基于主成分分析的丹江口水库支流水质评价[J].水电能源科学,2015,33(1):34-38.
[4]王成杰,张森.基于主成分和粒子群优化支持向量机的水质评价模型[J].环境工程学报,2014,8(10):4545-4549.
[5]王晴晴,陈星,常进.主成分分析法与熵值法结合在水质评价中的应用[J].人民长江,2015,46(8):10-13,18.
[6]孟宪萌,胡和平.基于熵权的集对分析模型在水质综合评价中的应用[J].水利学报,2009,40(3):257-262.
[7]颜惠琴,牛万红,韩惠丽.基于主成分分析构建指标权重的客观赋权法[J].济南大学学报:自然科学版,2017,31(6):519-523.
[8]赵克勤.集对分析与熵的研究[J].浙江大学学报:社会科学版,1992(2):68-75.
[9]莫崇勋,莫桂燕,阮俞理,等.五元集对分析在澄碧河流域径流分类中的运用[J].广西大学学报:自然科学版,2017,42(1):379-385.
[10]葛康,汪明武,陈光怡.基于集对分析与三角模糊数耦合的土壤重金属污染评价模型[J].土壤,2011,43(2):216-220.
[11]孙占超.基于云模型的水环境质量评价研究[D].西安:长安大学,2017.