Abstract
Residual chlorine and biomass concentrations are two important indicators of water quality in water distribution systems. To assist in the supply of safe drinking water to consumers, several process‐based models have been developed for predicting chlorine residual attenuation and bacterial regrowth in distribution networks. However, calibration of these models requires extensive and accurate data regarding numerous water quality parameters. In this research, an alternative modeling procedure that incorporates an artificial neural network was used to predict temporal chlorine residual and biomass concentrations at different nodes for five water distribution systems. The authors considered three types of algorithms based on feed‐forward neural networks: resilient back propagation, Levenberg‐Marquardt, and general regression. The models developed were tested for unseen data, and comparisons were made on the basis of the mean absolute error and coefficient of correlation.