Presently, concentrations of fecal indicator bacteria (FIB) in raw water sources are not known before water undergoes treatment, since analysis takes approximately 24h to produce results. Using data on water quality and environmental variables, models can be used to predict real time concentrations of FIB in raw water. This study evaluates the potentials of zero-inflated regression models (ZI), Random Forest regression model (RF) and adaptive neuro-fuzzy inference system (ANFIS) to predict the concentration of FIB in the raw water source of a water treatment plant in Norway. The ZI, RF and ANFIS faecal indicator bacteria predictive models were built using physico-chemical (pH, temperature, electrical conductivity, turbidity, color, and alkalinity) and catchment precipitation data from 2009 to 2015. The study revealed that pH, temperature, turbidity, and electrical conductivity in the raw water were the most significant factors associated with the concentration of FIB in the raw water source. Compared to the other models, the ANFIS model was superior (Mean Square Error=39.49, 0.35, 0.09, 0.23CFU/100ml respectively for coliform bacteria, E. coli, Intestinal enterococci and Clostridium perfringens) in predicting the variations of FIB in the raw water during model testing. However, the model was not capable of predicting low counts of FIB during both training and testing stages of the models. The ZI and RF models were more consistent when applied to testing data, and they predicted FIB concentrations that characterized the observed FIB concentrations. While these models might need further improvement, results of this study indicate that ZI and RF regression models have high prospects as tools for the real-time prediction of FIB in raw water sources for proactive microbial risk management in water treatment plants.
Keywords: Adaptive neuro-fuzzy inference system; Faecal indicator bacteria; Random forest; Zero-inflated models.
Copyright © 2018 Elsevier B.V. All rights reserved.