Environmental Science and Pollution Research

, Volume 22, Issue 8, pp 6208–6219 | Cite as

Prediction of water quality index in constructed wetlands using support vector machine

  • Reza Mohammadpour
  • Syafiq Shaharuddin
  • Chun Kiat Chang
  • Nor Azazi Zakaria
  • Aminuddin Ab Ghani
  • Ngai Weng Chan
Research Article

Abstract

Poor water quality is a serious problem in the world which threatens human health, ecosystems, and plant/animal life. Prediction of surface water quality is a main concern in water resource and environmental systems. In this research, the support vector machine and two methods of artificial neural networks (ANNs), namely feed forward back propagation (FFBP) and radial basis function (RBF), were used to predict the water quality index (WQI) in a free constructed wetland. Seventeen points of the wetland were monitored twice a month over a period of 14 months, and an extensive dataset was collected for 11 water quality variables. A detailed comparison of the overall performance showed that prediction of the support vector machine (SVM) model with coefficient of correlation (R2) = 0.9984 and mean absolute error (MAE) = 0.0052 was either better or comparable with neural networks. This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment. These methods simplify the calculation of the WQI and reduce substantial efforts and time by optimizing the computations.

Keywords

Support vector machine Constructed wetland Water quality index Neural networks Surface water 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Reza Mohammadpour
    • 1
  • Syafiq Shaharuddin
    • 1
  • Chun Kiat Chang
    • 1
  • Nor Azazi Zakaria
    • 1
  • Aminuddin Ab Ghani
    • 1
  • Ngai Weng Chan
    • 1
  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaNibong TebalMalaysia

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