Water Science and Engineering 2020, 13(1) 14-23 DOI:   https://doi.org/10.1016/j.wse.2020.03.005  ISSN: 1674-2370 CN: 32-1785/TV

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Constructed wetlands
Urban stormwater
Pollutant removal
Artificial neural networks (ANNs)
Principal component analysis (PCA)

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

Christopher Kiizaa, Shun-qi Pana,*, Bettina Bockelmann-Evansa, Akintunde Babatundea,b

a Hydro-environmental Research Centre, School of Engineering, Cardiff University, The Parade, Cardiff CF24 3AA, UK 
b School of Civil Engineering, University of Leeds, Leeds LS2 9J, UK


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.

Keywords Constructed wetlands   Urban stormwater   Pollutant removal   Artificial neural networks (ANNs)   Principal component analysis (PCA)  
Received 2019-05-28 Revised 2019-09-21 Online: 2020-03-30 
DOI: https://doi.org/10.1016/j.wse.2020.03.005
Corresponding Authors: Shun-qi Pan
Email: PanS2@cardiff.ac.uk
About author:


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