Journal
MARINE POLLUTION BULLETIN
Volume 64, Issue 11, Pages 2409-2420Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.marpolbul.2012.08.005
Keywords
Surface water; Kinta River; Water quality index; Artificial neural network; Three-layer perceptron; Quickprop algorithm
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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. (C) 2012 Elsevier Ltd. All rights reserved.
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