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Modelling constructed wetland treatment system performance

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  • Tomenko, Volodymyr
  • Ahmed, Sirajuddin
  • Popov, Viktor

Abstract

Multiple regression analysis (MRA) and two artificial neural networks (ANN) – multilayer perceptron (MLP) and radial basis function network (RBF) – were compared in terms of their accuracy and efficiency when applied to prediction of the biochemical oxygen demand (BOD) concentration at effluent and intermediate points of subsurface flow constructed treatment wetlands (CTW). The data used in this work was obtained from various hydraulic and BOD loading of a pilot CTW located in India and comprised of 91 patterns. The dataset was normalized and transformed using principal component analysis (PCA) in order to increase the efficiency of the modelling. At the modelling stage the most adequate models were determined by using systematic approach. The candidate ANN models were cross-validated to find optimal network architectures and values of training algorithm parameters. MRA performance was maximized by utilizing 14-fold cross-validation. MRA as well as ANN models were found to provide an efficient and robust tool in predicting CTW performance. MLP and RBF produced the most accurate results indicating strong potential for modelling of wastewater treatment processes.

Suggested Citation

  • Tomenko, Volodymyr & Ahmed, Sirajuddin & Popov, Viktor, 2007. "Modelling constructed wetland treatment system performance," Ecological Modelling, Elsevier, vol. 205(3), pages 355-364.
    • Handle: RePEc:eee:ecomod:v:205:y:2007:i:3:p:355-364
    DOI: 10.1016/j.ecolmodel.2007.02.030
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    Cited by:

    1. West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803, July.
    2. Yuhang Wang & Aibo Hao & Yue Quan & Mingji Jin & Wenhua Piao, 2023. "Analysis of the Degradation Characteristics of Chlorpyrifos in an Electrochemically Constructed Wetland Coupled System under Different Pesticide Exposure Conditions and Microbial Community Analysis," Sustainability, MDPI, vol. 15(22), pages 1-15, November.

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