基于综合主成分及径向基网络的环境质量评价

运用综合主成分分析对监测数据进行处理,集成径向基函数人工神经网络(RBF-ANN),参考国家环境质量评价标准设定RBF的学习样本,从而构建区域环境质量综合评价模型,对安徽省合肥市新站综合开发试验区进行环境质量综合评价。实例分析结果表明,运用综合主成分法可以精准的统计出一个区域的环境综合数据,而且在matlab环境下运用RBF-ANN模型既可以准确,客观的评定环境质量的等级,又可以表现其环境污染的具体程度,能在同一评价等级内对不同环境质量的评价对象进行更加细微的污染程度的比较。结果表明,合肥市新站综合开发试验区环境综合质量介于轻度污染和中度污染的标准极限值之间,属于中度污染。

Environmental Quality Assessment Based on Comprehensive Principal Component Analysis and Radial Basis Function Neutral Network Model

A comprehensive assessment model of regional environmental quality was built to investigate environmental quality of Xinzhan Comprehensive Developmental and Experimental Zone,Hefei,Anhui Province,which applied monitoring data processing with the method of comprehensive principal component analysis and a radial basis function artificial neural network(RBF-ANN)and leaning samples of RBF designed according to national standards of environmental quality assessment.The case study indicated that application of comprehensive principal component analysis can show the comprehensive data of an area accurately and under conditions of MATLAB,the model of RBF-ANN not only can evaluate the rank of environmental quality accurately and objectively,but also show details of environmental pollution and compare different objects of assessment which have different environmental qualities in the same degree.Results showed that the degree of comprehensive assessment on the Xinzhan Zone is medium pollution,which is between limit standard of light pollution and medium pollution.