桂林理工大学学报2012，Vol.32Issue(2)：189-194,6.DOI:10.3969/j.issn.1674-9057.2012.02.007
PCA-BP神经网络在流域水质评价中的应用
Application of PCA -BP Neural Network on Water Quality Evaluation in Major Drainage Basin
摘要
Abstract
In the application of BP neural network method on water quality evaluation in major drainage basin with multi-pollution characteristics, few training samples and validation samples can be found. An improved water quality evaluation method is introduced based on principal component analysis (PCA) - BP neural network. Pollution ratio is used to filter out a set of pollution date as indication to reflect water qualityy of the basin. The principal component method is applied to get pollution characteristics of drainage basin water quality and to solve the problem of few training samples. The model validation criteria is designed to solve the problem of no validation sample. Case study in the paper shows that the principal component PCA - BP neural network is suitable for the drainage basin of water quality evaluation, and the result is accurate and credible.关键词
主成分/BP神经网络/水质评价/大流域Key words
principal component analysis ( PCA)/ BP neural network/ water quality evaluation/ major drainage basin分类
资源环境引用本文复制引用
喻泽斌,施丽玲..PCA-BP神经网络在流域水质评价中的应用[J].桂林理工大学学报,2012,32(2):189-194,6.基金项目
广西科技攻关项目(桂科攻0816002-7) （桂科攻0816002-7）
