水力发电2017，Vol.43Issue(10)：1-4,4.
基于改进的BP神经网络水源地水质安全预测
Quality Evaluation and Prediction of Groundwater Drinking Sources Based on Improved BP Neural Network
摘要
Abstract
In order to solve the problems of the screening and selection of influencing factors in conventional BP neural network,an improved BP neural network model is proposed.Firstly,ten water quality monitoring indicators of groundwater drinking water sources in Yangquan,Shanxi are selected and the correlation coefficient is obtained by Pearson correlation analysis.Then the information index evaluation method is used to filter the simulation factors and the optimal simulation factors are obtained.Finally,the water quality prediction model is established by taking the optimal simulation factors as the input of BP neural network after determining the structure of BP neural network and the simulated factor (comprehensive index of water quality) as output sample.The simulation results show that the average relative error of predicted water quality comprehensive index is 3.80％ to actual values,and the average relative error of water quality index is zero.The forecasting accuracy is higher than that of traditional BP neural network model.关键词
水质安全预测/Pearson相关分析/信息指标评价法/BP神经网络Key words
water quality safety forecasting/Pearson correlation analysis/information index evaluation method/BP neural network分类
资源环境引用本文复制引用
张萌,赵志怀,司宏宇..基于改进的BP神经网络水源地水质安全预测[J].水力发电,2017,43(10):1-4,4.基金项目
山西省自然科学基金资助项目(2015021169) （2015021169）
