Using artificial neural network models for eutrophication prediction

摘要：

　　Artificial neural network (ANN),a data driven modeling approach,is proposed to predict the water quality indicators of Lake Fuxian,the deepest lake of southwest China.To determine the non-linear relationships between the water quality factors and the eutrophication indicators,several ANN models was chosen for the investigation.A commonly used back-propagation neural network model was used to relate the key factors that influence a number of water quality indicators such as dissolved oxygen (DO),total phosphorus (TP),chlorophyll-a (Chl-a),and secchi disk depth (SD) in Lake Fuxian.The measured data were fed to the input layer,representing forcing functions to control the in-lake bio-chemical processes.Eutrophication indicators such as DO,TN,Chl-a and SD were represented in the output layers.The results indicated that the back-propagation neural network model performs good in ten months prediction and the neural network is able to predict these indicators with reasonable accuracy.This study also suggested that the neural network is a valuable tool for lake management.

关键词： artificial neural network eutrophication water quality lake management

作者: Shouliang Huo Zhuoshi He Jing Su Beidou Xi Chaowei Zhu

作者单位: State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China

会议类型: 国际会议

会议名称: 2013 International Symposium on Environmental Science and Technology(2013环境科学与技术国际会议)

会议地点: 大连

会议语种:英文

页码: 244-248

在线出版日期: 2013-06-04（万方平台首次上网日期，不代表论文的发表时间）