Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water.

Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA and Cl) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N = 46-69%, r = 0.334-0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA and Cl can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N and r values for testing datasets reached 85-92% and 0.813-0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.