计算机技术与发展Issue(4)：235-238,242,5.DOI:10.3969/j.issn.1673-629X.2014.04.060
BP神经网络在长江水质COD预测中的应用
Application of BP Neural Network in Predicting COD of Yangtze River
摘要
Abstract
Water quality change is of nonlinear and dynamicity,it is a kind of complex time series data,therefore,the traditional linear pre-diction model cannot reflect the variation rule,and the prediction accuracy is low. For the problems of complex water quality change rule and high degree of nonlinear between factors,in order to improve the water quality prediction accuracy,introduce the BP neural network of improved algorithm into a model of COD,with pH,DO,NH3-N as input and COD as output,the prediction model of COD is estab-lished and tested. The research results show the linear correlation coefficient of COD between forecasting and the monitoring in the test samples is 0. 991. BP neural network has high forecast precision,fast convergence rate and the good generalization ability,which can bet-ter reflect the change rule between COD and impact factors.关键词
神经网络/水质/化学需氧量/溶解氧/氨氮Key words
neural network/water quality/COD/DO/NH3-N分类
资源环境引用本文复制引用
郭庆春,郝源,李雪,杜北方,张向阳..BP神经网络在长江水质COD预测中的应用[J].计算机技术与发展,2014,(4):235-238,242,5.基金项目
国家重点基础研究发展规划项目(2010CB833406) （2010CB833406）
国家自然科学基金资助项目(40975020,41075067) （40975020,41075067）
陕西省教育科学研究计划项目(12JK0123,12JK0414) （12JK0123,12JK0414）
