Using water quality parameters to prediction of the ion-based trihalomethane by an artificial neural network model

Trihalomethanes (THMs) are the first disinfectant by-products in the drinking water distribution network and are classified as potential carcinogens. The presence of THMs in chlorinated water depends on the pH, water temperature, contact time between water and chlorine, type and dose of disinfection, bromide ion concentration, and type and concentration of natural organic materials (NOMs). In the present study, the formation of THMs was evaluated by six simple and easy water quality parameters and modeled by an artificial neural network (ANN) approach through five water distribution networks (WDNs) and the Karoun River in Khuzestan province. The results of this study that was conducted from October 2014 to September 2015 showed that THM concentration ranged in five WDNs, including Shoushtar, Ahvaz (2), Ahvaz (3), Mahshahr, Khorramshahr, and total WDNs through N.D.-9.39 µg/L, 7.12-28.60, 38.16-67.00, 17.15-90.46, 15.14-29.99, and N.D.-156, respectively. The concentration of THMs exceeded Iran and EPA standards in many cases in Mahshahr and Khorramshahr WDNs. Evaluation of R², MSE, and RMSE showed the appropriate correlation between measured and modeled THMs, indicating a reasonable ANN potential for estimating THM formation in water sources.