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Artificial neural network modeling of the water quality index using land use areas as predictors


Citation

Gazzaz, Nabeel M. and Yusoff, Mohd Kamil and Ramli, Mohammad Firuz and Juahir, Hafizan and Aris, Ahmad Zaharin (2015) Artificial neural network modeling of the water quality index using land use areas as predictors. Water Environment Research, 87 (2). pp. 99-112. ISSN 1061-4303; ESSN: 1554-7531

Abstract / Synopsis

This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Environmental Studies
DOI Number: https://doi.org/10.2175/106143014X14062131179276
Publisher: Water Environment Federation
Keywords: Artificial neural network; Function approximation; Land use areas; Three-layer perceptron; Unweighted harmonic square mean; Water quality index; Weighted arithmetic mean
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 21 Sep 2016 08:20
Last Modified: 21 Sep 2016 08:20
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.2175/106143014X14062131179276
URI: http://psasir.upm.edu.my/id/eprint/43836
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