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Modelling source water quality in drinking water treatment systems could be useful for anticipating changes in specific raw water quality parameters. Those changes entail adjustments in drinking water treatment plant (DWTP) operations. Artificial intelligence (AI) has been used for modelling water quality for different purposes and has yielded reliable results. However, there has not yet been wide investigation of raw water quality modelling for treatment purposes using AI. For the first time, in this critical review, we analyzed AI models founded on machine learning techniques that are used for surface water quality modelling and which could be applied in the domain of source water treatment. In a novel approach, we convened an expert panel that helped us define the appropriate criteria for use in the selection of the papers for review. We analysed the selected papers according to several criteria, including the feasibility of input data generation, the modelled data applicability and the benefits and limitations. We evaluated whether the selected models could be applied to forecast raw water quality as decision support systems (DSS) in drinking water treatment. The highest rated were turbidity hourly models based on Support Vector Machines (SVM), as well as daily turbidity and pH models based on Artificial Neural Networks (ANN). We found there is a shortage of models used to specifically estimate raw water quality, which could assist in DSS at DWTPs. There should be an increased effort to model raw water quality, especially with AI models using hourly and sub-hourly time step.

Machine learning models with potential application to predict source water quality for treatment purposes: a critical review