title: Prediction of water quality index in constructed wetlands using support vector machine.
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 (R(2)) = 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.
authors.affiliation: River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300, Nibong Tebal, Penang, Malaysia, reza564@gmail.com.
authors.forename: Reza
authors.initials: R
authors.lastname: Mohammadpour
authors.forename: Syafiq
authors.initials: S
authors.lastname: Shaharuddin
authors.forename: Chun Kiat
authors.initials: CK
authors.lastname: Chang
authors.forename: Nor Azazi
authors.initials: NA
authors.lastname: Zakaria
authors.forename: Aminuddin
authors.initials: A
authors.lastname: Ab Ghani
authors.forename: Ngai Weng
authors.initials: NW
authors.lastname: Chan
mesh_terms: D000818:Animals; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D060388:Support Vector Machine; D014873:Water Pollutants / Q000737:chemistry*; D060753:Water Quality / Q000592:standards*; D053833:Wetlands
chemical_list: D014873:Water Pollutants