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.

journal_name

Mar Pollut Bull

authors

Gazzaz NM,Yusoff MK,Aris AZ,Juahir H,Ramli MF

doi

10.1016/j.marpolbul.2012.08.005

subject

Has Abstract

pub_date

2012-11-01 00:00:00

pages

2409-20

issue

11

eissn

0025-326X

issn

1879-3363

pii

S0025-326X(12)00404-3

journal_volume

64

pub_type

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