The modelling of water treatment processes is challenging because of its complexity, nonlinearity, and numerous contributory variables, but it is of particular importance since water of low quality causes health-related and economic problems which have a considerable impact on people’s daily lives. Linear and nonlinear modelling methods are used here to model residual aluminium and turbidity in treated water, using both laboratory and process data as input variables. The approach includes variable selection to find the most important factors affecting the quality parameters. Correlations of∼0.7–0.9 between the modelled and real values for the target parameters were ultimately achieved. This data analysis procedure seems to provide an efficient means of modelling the water treatment process and defining its most essential variables.