Enhancing source water quality predictions to improve treatment by integrating watershed data on water quality, river flow and rainfall into interpretable machine learning algorithms

Abstract

Raw water quality used for municipal water treatment is impacted during and after rainfall events and the resultant changes in river flow. Recently, raw water quality parameters such as turbidity have been modeled and predicted using machine learning algorithms, based on environmental, hydrological, and meteorological information as input variables. Our research aims to integrate upstream water quality with river flow and watershed rainfall data into interpretable machine learning algorithms to enhance raw water turbidity predictions. Such predictions would allow water utility operators to anticipate the required adjustments during water treatment processes. First, we estimated lag-times between the upstream input variables of rainfall in watershed, river flow and raw water turbidity, and the output targeted variable of downstream raw water turbidity. Then, we used a XGBoost technique to predict raw water turbidity using upstream water quality along with river flow and watershed rainfall data. Finally, the overall importance of every input variable was estimated using a SHAP (SHapley Additive exPlanations) strategy. Results showed that the upstream raw water turbidity is the most important input variable, followed by river flow. Best performance metrics and time series visual inspection of modeled variables showed that integrating upstream raw water quality data leads to enhanced raw water predictions. These results could open possibilities for developing and implementing regional raw water quality modeling that can feed Weather-Event-Water-Treatment–Early-Warning-Systems (WEWT-EWS). Future research could improve raw water quality prediction horizons and include interannual data.