文献类型：期刊文献

英文题名：Modeling total phosphorus removal in an aquatic environment restoring horizontal subsurface flow constructed wetland based on artificial neural networks

作者：Li, Wei[1] Zhang, Yan[1,2] Cui, Lijuan[1] Zhang, Manyin[1] Wang, Yifei[1]

第一作者：李卫

通信作者：Cui, LJ[1]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, Beijing 100091, Peoples R China;[2]Peking Univ, Dept Environm Engn, Minist Educ, Key Lab Water & Sediment Sci, Beijing 100871, Peoples R China

年份：2015

卷号：22

期号：16

起止页码：12362-12369

外文期刊名：ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

收录：;EI(收录号：20242616456891);Scopus(收录号：2-s2.0-84937977341);WOS:【SCI-EXPANDED(收录号:WOS:000358579700036)】；

基金：This study was funded by the National Nonprofit Institute Research Grant of the Chinese Academy of Forestry entitled "Dynamic mechanisms of phosphorus removal in subsurface constructed wetlands" (CAFINT2013C13) and the Fundamental Research Funds for the Central Non-profit Research Institution of CAF "N removal mechanism of subsurface constructed wetland in health wetland" (CAFYBB2014QA029). We are grateful to all the members of the research team for their helpful comments and advice.

语种：英文

外文关键词：Aquatic environment restoration; Horizontal subsurface constructed wetland; Total phosphorus; Artificial neural networks; Model

摘要：A horizontal subsurface flow constructed wetland (HSSF-CW) was designed to improve the water quality of an artificial lake in Beijing Wildlife Rescue and Rehabilitation Center, Beijing, China. Artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were used to model the removal of total phosphorus (TP). Four variables were selected as the input parameters based on the principal component analysis: the influent TP concentration, water temperature, flow rate, and porosity. In order to improve model accuracy, alternative ANNs were developed by incorporating meteorological variables, including precipitation, air humidity, evapotranspiration, solar heat flux, and barometric pressure. A genetic algorithm and cross-validation were used to find the optimal network architectures for the ANNs. Comparison of the observed data and the model predictions indicated that, with careful variable selection, ANNs appeared to be an efficient and robust tool for predicting TP removal in the HSSF-CW. Comparison of the accuracy and efficiency of MLP and RBF for predicting TP removal showed that the RBF with additional meteorological variables produced the most accurate results, indicating a high potentiality for modeling TP removal in the HSSF-CW.