Random forest tree for predicting fecal indicator organisms in drinking water supply

Variety of modeling techniques have been widely applied for predicting levels of fecal indicator organisms in raw water. However, deficiencies in the performances of some methods make it difficult for implementation in full-scale water supply systems. This study examines the efficiency of random forest (RF) which is made up of a number of decision trees in the prediction of fecal indicator organisms in raw water based on records of conductivity, pH, color, turbidity taken from a drinking water source in Bergen, Norway, as well as seasons. Results of the study indicate that the method is capable of estimating important variations in levels of the microorganisms in the raw water with acceptable accuracy. Color of water and the effect of autumn season were the most important in explaining the variations in the levels of the coliform bacteria, intestinal enterococci and E. coli in raw water in both the full and the reduced models. Considerable reduction in the model out-of-bag sample error was achieved in the reduced models, where only two most important variables were used as predictors. With further research aimed at improving the estimation error, the random forest method can be a reliable tool for real time prediction of potential levels of microorganisms in raw water.