Kiiza, C, Pan, S-Q, Bockelmann-Evans, B et al. (1 more author) (2020) Predicting pollutant removal in constructed wetlands using artificial neural networks (ANNs). Water Science and Engineering, 13 (1). pp. 14-23. ISSN 1674-2370
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Hohai University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Constructed wetlands; Urban stormwater; Pollutant removal; Artificial neural networks (ANNs); Principal component analysis (PCA) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jun 2020 14:42 |
Last Modified: | 30 Jun 2020 14:42 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.wse.2020.03.005 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162598 |