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A data-driven methodology for modelling the raw water quality supplying a treatment plant, after rainfall and river flow rate events
Abstract
Particles and natural organic matter (NOM) are undesirable contaminants in raw water (RW) that must be removed by drinking water treatment plants (DWTP). To ensure effective treatment, DWTPs monitor surrogate parameters such as turbidity (for particles) and UV254 absorbance (for NOM). Rainfall and subsequent peak flow events in watersheds can lead to RW quality degradation, requiring adjustments to the dosage of chemicals at DWTPs. However, changes in RW quality may take place hours or days after a rainfall, which can complicate decision-making for DWTP operations. This study aims to develop a procedure for selecting input variables and modelling RW quality after rainfall and river flow peak events. Spearman cross-correlation analyses were conducted on several both rain gauge and flow rate time series in a watershed, along with RW data collected at a water intake. The analyses revealed that RW turbidity and UV absorbance increased at different time lags after rainfalls and flow peaks. The input variables with the highest correlations between time-lagged rainfall and river flow were used to predict turbidity and UV254 using two machine learning models. The study found that the maximum correlation between flow peaks and turbidity was observed after a few hours, while for UV absorbance, it was observed after a few days. This difference in behaviour adds complexity to DWT practices, which must consider these factors in operational schemes, such as adjusting chemical dosages. The results of the present study will help in developing more effective chemical dosing strategies to remove key contaminants.
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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.
Water quality and treatment. A handbook on drinking water
- J K Edzwald
Edzwald, J. K. 2011. Water quality and treatment. A handbook on drinking water. (AWWA, ed.).
McGraw Hill.