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

doi: 10.1016/j.wse.2020.03.005

  • 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

More Information
  • Corresponding author: Shun-qi Pan
  • Received Date: 2019-05-28
  • Rev Recd Date: 2019-09-21
    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.

     

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