Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)  

作　　者：Christopher Kiiza Shun-qi Pan Bettina Bockelmann-Evans Akintunde Babatunde 

机构地区：[1]Hydro-environmental Research Centre,School of Engineering,Cardiff University,The Parade,Cardiff CF243AA,UK [2]School of Civil Engineering,University of Leeds,Leeds LS29JT,UK

出　　处：《Water Science and Engineering》2020年第1期14-23,共10页水科学与水工程（英文版）

基　　金：This research was partly supported by the UK Engineering and Physical Sciences Research Council(EPSRC)Studentship and Asset International,who provided the HDPE materials used to build bespoke constructed wetlands.

摘　　要：Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs)for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN)method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA)and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.

关 键 词：CONSTRUCTED WETLANDS Urban STORMWATER POLLUTANT removal Artificial neural networks(ANNs) Principal component analysis(PCA) 

分 类 号：X703[环境科学与工程—环境工程] TP183[自动化与计算机技术—控制理论与控制工程]