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

Kiiza, Christopher, Pan, Shun-qi ORCID:, Bockelmann-Evans, Bettina ORCID: and Babatunde, Akintunde 2020. Predicting pollutant removal in constructed wetlands using artificial neural networks (ANNs). Water Science and Engineering 13 (1) , pp. 14-23. 10.1016/j.wse.2020.03.005

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Advanced Research Computing @ Cardiff (ARCCA)
Publisher: Editorial Board of Water Science and Engineering
ISSN: 1674-2370
Date of First Compliant Deposit: 6 April 2020
Date of Acceptance: 21 September 2019
Last Modified: 06 Feb 2024 09:35

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