title: Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors.
abstract: This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)(1) for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r=0.977, p<0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.
authors.affiliation: Department of Environmental Sciences, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 Serdang, Selangur Darul Ehsan, Malaysia. NabeelMGazzaz@Yahoo.com
authors.forename: Nabeel M
authors.initials: NM
authors.lastname: Gazzaz
authors.forename: Mohd Kamil
authors.initials: MK
authors.lastname: Yusoff
authors.forename: Ahmad Zaharin
authors.initials: AZ
authors.lastname: Aris
authors.forename: Hafizan
authors.initials: H
authors.lastname: Juahir
authors.forename: Mohammad Firuz
authors.initials: MF
authors.lastname: Ramli
mesh_terms: D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D008296:Malaysia; D016571:Neural Networks, Computer; D045483:Rivers / Q000737:chemistry; D014876:Water Pollution / Q000032:analysis* / Q000706:statistics & numerical data; D060753:Water Quality / Q000592:standards*