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

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 ,

Full text not available from this repository.
Official URL:


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.

Item Type: Article
Additional Information: Department of Chemistry, Faculty of Science Building, University of Malaya, 50603 Kuala Lumpur, MALAYSIA
Uncontrolled Keywords: Artificial neural network, Kinta river, River pollution, Sensitivity analysis, Water quality, Water quality index
Subjects: Q Science > QD Chemistry
Divisions: Faculty of Science > Department of Chemistry
Depositing User: Miss Malisa Diana
Date Deposited: 13 May 2013 01:49
Last Modified: 25 Oct 2019 09:11

Actions (login required)

View Item View Item