神经网络在次级河流回水区叶绿素a浓度预测中的应用  

Application of the neural network in prediction for chlorophyll-a in branch backwater region

作　　者：罗固源[1] 郑剑锋[1] 许晓毅[1] 曹佳[2] 舒为群[2] 

机构地区：[1]重庆大学三峡库区生态环境教育部重点试验室,重庆400045 [2]第三军医大学军事预防医学院,重庆400038

出　　处：《环境工程学报》2009年第2期372-376,共5页Chinese Journal of Environmental Engineering

基　　金：科技部国际合作项目(2007DFA90660);重庆市科技攻关计划项目(CSTC,2006AB7020CSTC,2006AA7003)

摘　　要：以长江次级河流之一的临江河为研究对象,探讨神经网络应用于次级河流回水区叶绿素a浓度短期预测的可行性。利用主成分分析法(PCA)选取对叶绿素a浓度影响较大的指标,在这些指标数据的基础上建立RBF神经网络模型。网络训练和测试的结果表明,模型模拟精度较高,说明RBF神经网络模型可以用于次级河流回水区叶绿素a浓度的短期预测。通过对临江河回水区叶绿素a影响因子的分析,表明控制水体中磷含量应是临江河回水区富营养化防治的重点。Taking Linjiang river which is a branch of Yangtze River as research object to evaluate the feasibility of neural network model for simulating chlorophyll-a trend in branch backwater region. By using the method of principal component analysis （PCA） to select the main indexes which affect the chlorophyll-a trend, the RBF neural network model was created based on the database of indexes. The training and testing results of model indicated that the simulating accuracy of model was high; it showed that the RBF neural network model could be used for simulating the chlorophyll-a short-term trend in branch backwater region. By analyzing the influencing factors of cblorophyll-a in Linjiang river backwater region, the result showed that controlling phosphorus content would be important to prevent and control Linjiang river backwater region＇ s eutrophication.

关 键 词：次级河流 回水区 叶绿素a神经网络 主成分分析 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] X171.1[自动化与计算机技术—控制科学与工程]