Artificial neural networks have been shown to be able to approximate any continuous non-linear functions and have been used to build data base empirical models for non-linear processes. In this study, neural networks models were used to model the daily river flows or discharged in Langat River, Malaysia. Two possible ways of modelling were implemented which is by time series prediction and by the dynamics function of the system which include the past value of the discharged and also the rainfall in the input. The sum square error (SSE), residue analysis and correlation coefficient based on the observed and prediction output is chosen as the criteria of selection of which models is appropriate. It was found that the developed neural networks models using dynamics function provided satisfactory model discharges.

This study aims to identify the possible sources in drinking water parameters heavy metal and organic parameters (HMOPs) and spatial variation between untreated water and treated water at Federal Territory of Kuala Lumpur water treatment plant. The indicator HMOPs in drinking water in Kuala Lumpur were selected as parameters to discriminate the possible source of water treatment plants (WTPs) pollutant. Chemometric technique such as principal component analysis (PCA) and discriminant analysis (DA) was identified based on the five years’ availability data starting from 2012 to 2016. PCA were used to identify the most significant parameters which are highlighted eleven strong factors loading of parameter respectively out of sixteen for PCs and classified as possible sources in WTPs. Continue with DA analysis that is successful distinguish two categories region in WTP using the forward stepwise and backward stepwise with significant amount is 98.46%. From this study, we can conclude that this chemometric is the best technique of analysis to get a lot of information such as identify possible sources of pollutant and discriminant of two station sampling categories that will give novelty to Malaysian Ministry of Health (MOH) and collaboration agency in National Drinking Water Quality Surveillances Program (NDWQSP).