基于改进模糊综合评价法的太湖富营养化评价  

Eutrophication Evaluation of Taihu Lake Based on Improved Fuzzy Comprehensive Evaluation Method

作　　者：聂青[1] 纪小敏 周毅[1] 

机构地区：[1]江苏省水文水资源勘测局,江苏南京210029

出　　处：《水电能源科学》2015年第8期29-32,共4页Water Resources and Power

摘　　要：针对传统模糊综合评价法构造隶属函数和计算权重方法存在的一些问题,提出将隶属函数改为非线性的S函数,并将指标值的离散程度添加到计算权重中,从而使模糊综合评价法更适用于富营养化评价。分别采用改进的模糊综合评价法、灰色关联度法、Hopfield神经网络模型对太湖水质进行富营养化评价,结果表明这三种方法评价结果接近,但当评价值接近于某级别标准值时,可能会相差一个等级,因此必要时可采用多种评价方法进行全面研究。Aiming at the problems of traditional fuzzy comprehensive evaluation method in membership function and weight,this article changes the membership functions into nonlinear S-type function and adds the discrete degree of index value to the calculation of weight so that the fuzzy comprehensive evaluation method is more suitable for eutrophication evaluation.Improved fuzzy comprehensive evaluation method,grey correlation method,Hopfield neural network model are used o evaluate the eutrophication degree of water quality in Taihu Lake.It shows that the evaluation results of these methods are basically close.When the evaluation value is close to a certain level of standard value,it may be a difference level for these methods.Therefore,it is necessary to adopt a variety of evaluation methods to conduct a comprehensive study.

关 键 词：富营养化 改进的模糊综合评价法 灰色关联度法 HOPFIELD神经网络模型 太湖 

分 类 号：X824[环境科学与工程—环境工程]