Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia

Water quality index (WQI) serves as the basis for environment assessment of watercourse in relation to pollution load categorization and designation of classes and beneficial uses as provided by Interim National Water Quality Standards (INWQS) in Malaysia. This index is calculated based on six param...

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Main Authors: Juahir, H., Saadudin, S.B., Abdullah, B., Kasim, M.F., Zain, Sharifuddin Md, Retnam, A., Zali, M.A.
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Published: 2011
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Online Access:http://eprints.um.edu.my/6059/
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spelling my.um.eprints.60592019-10-25T09:11:59Z http://eprints.um.edu.my/6059/ Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia Juahir, H. Saadudin, S.B. Abdullah, B. Kasim, M.F. Zain, Sharifuddin Md Retnam, A. Zali, M.A. QD Chemistry Water quality index (WQI) serves as the basis for environment assessment of watercourse in relation to pollution load categorization and designation of classes and beneficial uses as provided by Interim National Water Quality Standards (INWQS) in Malaysia. This index is calculated based on six parameters DO, BOD, COD, pH, NH 3-NL and SS. This research was need as it will give the preliminary judgement on the importance of each water quality parameter for WQI calculation at the Kinta River, Malaysia. This study revealed the used of sensitivity analysis based on ANN to evaluate the significant of each parameter for WQI determination. Sensitivity analysis was carried out for seven models (ANN-WQI-AP, ANN-WQI-LDO, ANN-WQI-LBOD, ANN-WQI-LCOD, ANN-WQI-LpH and ANN-WQI-LNH 3-NL) and a model performance criterion (R 2, RMSE and SSE) was used for model performance evaluation. DO, SS and NH 3-NL were selected as the best input models for WQI prediction. The ANN-WQI-LDO, ANN-WQI-LSS and ANN-WQI-LNH 3-NL model have R 2 values of 0.8301, 0.9265 and 0.9369 respectively; RMSE values of 4.888, 3.214 and 2.978 respectively; SSE values of 3106.534, 1343.286 and 1152.902 respectively. The low R 2 values and higher RMSE and SSE value compared to the ANN-WQI-AP model suggest the importance of these three parameters significantly affect the fitness and residual measurement of the ANN models in WQI prediction. The result also suggests that water quality of Kinta River was affected by agricultural activities and vicinity animal farm. Moreover the use of less parameter for WQI is much more applicable for our water resource management since its time and cost consuming. © IDOSI Publications, 2011. 2011 Article PeerReviewed Juahir, H. and Saadudin, S.B. and Abdullah, B. and Kasim, M.F. and Zain, Sharifuddin Md and Retnam, A. and Zali, M.A. (2011) Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia. World Applied Sciences Journal, 14. pp. 60-65. ISSN 18184952 http://www.scopus.com/inward/record.url?eid=2-s2.0-84864954432&partnerID=40&md5=a501120f7194f6db46bc1a49a79587e9
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QD Chemistry
spellingShingle QD Chemistry
Juahir, H.
Saadudin, S.B.
Abdullah, B.
Kasim, M.F.
Zain, Sharifuddin Md
Retnam, A.
Zali, M.A.
Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia
description Water quality index (WQI) serves as the basis for environment assessment of watercourse in relation to pollution load categorization and designation of classes and beneficial uses as provided by Interim National Water Quality Standards (INWQS) in Malaysia. This index is calculated based on six parameters DO, BOD, COD, pH, NH 3-NL and SS. This research was need as it will give the preliminary judgement on the importance of each water quality parameter for WQI calculation at the Kinta River, Malaysia. This study revealed the used of sensitivity analysis based on ANN to evaluate the significant of each parameter for WQI determination. Sensitivity analysis was carried out for seven models (ANN-WQI-AP, ANN-WQI-LDO, ANN-WQI-LBOD, ANN-WQI-LCOD, ANN-WQI-LpH and ANN-WQI-LNH 3-NL) and a model performance criterion (R 2, RMSE and SSE) was used for model performance evaluation. DO, SS and NH 3-NL were selected as the best input models for WQI prediction. The ANN-WQI-LDO, ANN-WQI-LSS and ANN-WQI-LNH 3-NL model have R 2 values of 0.8301, 0.9265 and 0.9369 respectively; RMSE values of 4.888, 3.214 and 2.978 respectively; SSE values of 3106.534, 1343.286 and 1152.902 respectively. The low R 2 values and higher RMSE and SSE value compared to the ANN-WQI-AP model suggest the importance of these three parameters significantly affect the fitness and residual measurement of the ANN models in WQI prediction. The result also suggests that water quality of Kinta River was affected by agricultural activities and vicinity animal farm. Moreover the use of less parameter for WQI is much more applicable for our water resource management since its time and cost consuming. © IDOSI Publications, 2011.
format Article
author Juahir, H.
Saadudin, S.B.
Abdullah, B.
Kasim, M.F.
Zain, Sharifuddin Md
Retnam, A.
Zali, M.A.
author_facet Juahir, H.
Saadudin, S.B.
Abdullah, B.
Kasim, M.F.
Zain, Sharifuddin Md
Retnam, A.
Zali, M.A.
author_sort Juahir, H.
title Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia
title_short Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia
title_full Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia
title_fullStr Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia
title_full_unstemmed Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia
title_sort sensitivity analysis for water quality index (wqi) prediction for kinta river, malaysia
publishDate 2011
url http://eprints.um.edu.my/6059/
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864954432&partnerID=40&md5=a501120f7194f6db46bc1a49a79587e9
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score 13.160551