Using artificial intelligence models to support water quality prediction in water distribution networks

Abstract Water chlorination is the most used disinfection method in water distribution networks (WDNs). Nonetheless, water quality parameters, including chlorine concentration, are not available at every point of the WDN, although such information is of direct relevance to drive the operation at water treatment plants to keep the correct chlorine residual through the system. This work proposes the use of data-driven models, i.e., Artificial Neural Networks and Evolutionary Polynomial Regression, to predict the water quality parameters in most areas of a WDN, using water quality data measures at few sampling points. The study is demonstrated on the case studies of the trunk network of Bogota’s water distribution system.