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Open Access Open Badges Research article

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

Mohd Fahmi Mohd Nasir1, Munirah Abdul Zali1, Hafizan Juahir1*, Hashimah Hussain2, Sharifuddin M Zain3 and Norlafifah Ramli4

Author Affiliations

1 Department of Environmental Sciences, Faculty of Environmental Studies, UPM Serdang, Selangor, Malaysia

2 Department of Environment, Federal Government Administrative Centre, Environment Institute of Malaysia, Putrajaya, Malaysia

3 Department of Chemistry, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia

4 Surface Water Monitoring Unit, Water and Marine Division, Department of Environment Malaysia, Federal Government Administrative Centre, Putrajaya, Malaysia

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Iranian Journal of Environmental Health Science & Engineering 2012, 9:18  doi:10.1186/1735-2746-9-18

Published: 10 December 2012


Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management.

Water quality; Receptor modeling; Multiple linear regression (MLR); Artificial neural network (ANN)