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

Recent techniques in the management of surface river water have been expanding the demand on the methodthat can provide more representative of multivariate data set. a proper technique of the architecture of artificialneural network (ann) model and multiple linear regression (mlr) provides an advance tool for surface watermodeling and forecasting. the development of receptor model was applied in order to determine the majorsources of pollutants at kuantan river basin, malaysia. thirteen water quality parameters were used in principalcomponent analysis (pca) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical andmineral changes and erosion are successfully developed for modeling purposes. two models were compared interms of efficiency and goodness-of-fit for water quality index (wqi) prediction. the results show that apcs-annmodel 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 dominantpollutant 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 receptormodeling of apcs-ann can be used to solve various constraints in environmental problem that exist betweenwater distribution variables toward appropriate water quality management.