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

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...

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Main Authors: Gazzaz, Nabeel M., Yusoff, Mohd Kamil, Ramli, Mohammad Firuz, Juahir, Hafizan, Aris, Ahmad Zaharin
Format: Article
Language:English
Published: Water Environment Federation 2015
Online Access:http://psasir.upm.edu.my/id/eprint/43836/1/Artificial%20neural%20network%20modeling%20of%20the%20water%20quality%20index%20using%20land%20use%20areas%20as%20.pdf
http://psasir.upm.edu.my/id/eprint/43836/
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spelling my.upm.eprints.438362016-09-21T08:20:00Z http://psasir.upm.edu.my/id/eprint/43836/ Artificial neural network modeling of the water quality index using land use areas as predictors Gazzaz, Nabeel M. Yusoff, Mohd Kamil Ramli, Mohammad Firuz Juahir, Hafizan Aris, Ahmad Zaharin 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. Water Environment Federation 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43836/1/Artificial%20neural%20network%20modeling%20of%20the%20water%20quality%20index%20using%20land%20use%20areas%20as%20.pdf 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 10.2175/106143014X14062131179276
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Gazzaz, Nabeel M.
Yusoff, Mohd Kamil
Ramli, Mohammad Firuz
Juahir, Hafizan
Aris, Ahmad Zaharin
spellingShingle Gazzaz, Nabeel M.
Yusoff, Mohd Kamil
Ramli, Mohammad Firuz
Juahir, Hafizan
Aris, Ahmad Zaharin
Artificial neural network modeling of the water quality index using land use areas as predictors
author_facet Gazzaz, Nabeel M.
Yusoff, Mohd Kamil
Ramli, Mohammad Firuz
Juahir, Hafizan
Aris, Ahmad Zaharin
author_sort Gazzaz, Nabeel M.
title Artificial neural network modeling of the water quality index using land use areas as predictors
title_short Artificial neural network modeling of the water quality index using land use areas as predictors
title_full Artificial neural network modeling of the water quality index using land use areas as predictors
title_fullStr Artificial neural network modeling of the water quality index using land use areas as predictors
title_full_unstemmed Artificial neural network modeling of the water quality index using land use areas as predictors
title_sort artificial neural network modeling of the water quality index using land use areas as predictors
publisher Water Environment Federation
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/43836/1/Artificial%20neural%20network%20modeling%20of%20the%20water%20quality%20index%20using%20land%20use%20areas%20as%20.pdf
http://psasir.upm.edu.my/id/eprint/43836/
_version_ 1643833683100041216
score 13.211869

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