• 关键词：水质评价  方差分析  聚类分析  径向基神经网络  太子河

• 基金项目：国家重点基础研究发展计划(973)项目(No2005CB724205);国家留学基金资助研究生项目(No2006100766)

• 周丰
• 北京大学环境学院, 北京 100871

• 郭怀成
• 北京大学环境学院, 北京 100871

• 刘永
• 北京大学环境学院, 北京 100871

• 罗定贵
• 1. 北京大学环境学院, 北京 100871; 2. 东华理工学院土木与环境工程系, 抚州 344000

• 王真
• 北京大学环境学院, 北京 100871

• 摘要：提出了一种基于多元统计分析和RBFNNs的水质评价方法,其可适用于大尺度、多断面、长时间的水质评价工作,旨在补充以往相关研究工作.其主要程序为:利用方差分析对各断面多年水质监测样本进行时间与空间尺度上的显著差异性分析,识别出具有显著差异的样本,然后通过层次聚类分析把上述样本进行聚类分组,最后应用径向基神经网络对各组样本进行水质评价,并把此评价结果再分解到各断面各时段,此方法的特点为在不损失信息的前提下能大大减轻水质评价工作量,且客观可信、分辨率高,并能综合反映总体与个别特征.以辽宁省太子河为例,通过方差分析将2001～2003年6个断面的144个样本归纳为存在显著差异的74个样本,再通过聚类分析得到9个相似组,相应的水质评价结果为2.7394、4.4306、4.0994、2.777、4.2192、4.1214、4.4129、4.4259、4.4359,与传统单项指数法的结果基本一致.具体落实到各断面,结果表明,2001～2003年间太子河大部分断面的水质处于IV类以上.

• Abstract：Aimed to supplement previous researches, this paper proposed a new assessment method of surface water quality based on multivariate statistical analysis and Radial Basis Function Neural Networks, which was useful for the large-scale and long-term monitoring. The main procedures of this approach include: (1) analyzing the temporal and spatial differences of independent samples according to analysis of variance (ANOVA), and recognizing the samples which were statistically significantly different between each others; (2) grouping the former samples into clusters on the basis of similarities within a cluster and dissimilarities between different clusters based on hierarchical cluster analysis (HCA); (3) modeling the appropriate Radial Basis Function neural networks to evaluate the surface water quality of each class, then feeding back this results to every original samples. Moreover, its particular characteristics were that it could reduce the workload in assessment and comprehensively represent both holistic condition and individual’s, and its result was objective and discriminative. The proposed method was applied to water quality assessment of Taizi river in Liaoning Province, China. The 144 original samples of six monitoring sites from 2001 to 2003 were divided into 74 significantly different samples and then into 9 clusters, and their results of water quality assessment were 2.7394, 4.4306, 4.0994, 2.777, 4.2192, 4.1214, 4.4129, 4.4259, and 4.4359, which was basically consistent with traditional simple index method. Besides, the water quality condition of each monitoring sites in Taizi River was mostly worse than Class IV.

• Key words：water quality assessment  aanalysis of variance  cluster analysis  Radial Basis Function Neural Networks  Taizi River

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