Pattern recognition approach for spatial and temporal variation analysis of surface water quality of Klang River Basin, Malaysia

In Malaysia, the hydrology and surface water quality of a river are often discussed among the city dwellers especially in the urban area as a consequence from the flash flood scenario during the rain event seasons. This study was conducted in Klang River in attempt to interpret the relationship between hydrological and surface water quality parameters, to estimate the pollution loading in the river with and without the utilization of the hydrological data and to ascertain the input and output parameters of the water quality data using the artificial neural network (ANN). The data was collected from the Department of Environment (DOE) and Department of Irrigation and Drainage (DID), Malaysia. Approximately 30 water quality parameters, rainfall, discharge and direct runoff data were considered for data analysis. This study integrates statistical tools such as non-parametric analysis, multivariate methods and ANN modeling in order to describe the spatial and temporal variation of water quality at Klang River Basin. This study revealed that among the 24 rainfall stations, Rs11 and Rs14 indicate the highest variations of rainfall with Rs02 exhibiting a downward trend with rainfall while the other stations indicate no trend. Among all the variables, temperature (TEMP) and mercury (Hg) were indicate the highest correlation value at p<0.05 (R=-0.245 and R=0.295) with rainfall while other stations have no significant relationship with rainfall. Despite that, river discharge illustrate Ds03 and Ds04 having an upward trend with only dissolved oxygen (DO), ammoniacal nitrogen (NH3N), TEMP, conductivity (COND), salinity (SAL), total solid (TS), chloride (Cl-), phosphate (PO43-), magnesium (Mg) and methylene blue active substances (MBAS) significantly (p