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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Water Environment Federation
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43836/ 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 |
| _version_ | 1848850336211533824 |
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| author | Gazzaz, Nabeel M. Yusoff, Mohd Kamil Ramli, Mohammad Firuz Juahir, Hafizan Aris, Ahmad Zaharin |
| author_facet | Gazzaz, Nabeel M. Yusoff, Mohd Kamil Ramli, Mohammad Firuz Juahir, Hafizan Aris, Ahmad Zaharin |
| author_sort | Gazzaz, Nabeel M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T10:04:40Z |
| format | Article |
| id | upm-43836 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:04:40Z |
| publishDate | 2015 |
| publisher | Water Environment Federation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/43836/ http://psasir.upm.edu.my/id/eprint/43836/ 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 |