BP神经网络模型在太湖出入湖河流水质预测中的应用  

APPLICATION OF BP NEURAL NETWORK MODEL IN PREDICTING WATER QUALITY OF RIVERS INFLOWING TO AND OUTFLOWING FROM TAIHU LAKE

作　　者：马正华[1] 王腾[1] 杨彦[1] 张亦含[1] 

机构地区：[1]常州大学信息科学与工程学院,江苏常州213164

出　　处：《计算机应用与软件》2013年第11期172-175,共4页Computer Applications and Software

基　　金：江苏省科技支持计划工业部分项目(BE2011061)

摘　　要：利用BP(Back Propagation)神经网络模型预测太湖出入湖河道水质污染指数,为太湖流域管理规划平台提供决策支持。建立三层BP神经网络预测模型,以1996年到2004年太湖例行环境监测数据为基础,进行水质预测,研究出入湖河道与太湖水质变化的关系。对太湖2005年的水质情况进行预测,结果表明,2005年水质污染情况有所改善,基本为V类水质,符合太湖水质污染情况发展态势。传统的数学建模方法要首先确定待定参数,而在实际应用中,许多不可测量估计的因素会对结果造成很大的影响。BP神经网络适应性较好、预测精度较高,能较好地反应水质指标的内在变化规律,为控制水环境污染提供科学依据。In order to provide decision-making support to management and planning platform of Taihu Lake basin, we use BP （ back propagation） neural network model to predict the water contamination index of rivers inflowing to and outflowing from Taihu Lake. We establish a three-layer BP neural network prediction model to predict water quality on the basis of routine environmental monitoring data of Taihu from year 1996 to year 2004, and study the relationship between inflowing and outilowing rivers and the variation of water quality of Taihu. The water quality of Taihu in year 2005 was predicted, the result showed that in 2005 the water quality pollution had some amelioration than before, generally in grade V water quality, and was in line with the development trend of Taihu water pollution situation. Traditional mathematical modelling method requires making sure the unknown parameters first, but in practical application, multiple unmeasurable and unpredictable factors will greatly affect the result. BP neural network method has good adaptability and higher precision, it can better reflect the inherent variation rules of water quality indexes, and provides scientific basis for controlling the water environmental pollution.

关 键 词：BP神经网络 太湖 出入湖河道 综合污染指数 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]