Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models

This paper describes the application of multilayer perceptron (MLP), radial basis network and adaptive neuro-fuzzy inference system (ANFIS) models for computing dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) levels in the Karoon River (Iran). Nine input water quality variables including EC, PH, Ca, Mg, Na, Turbidity, PO₄, NO₃ and NO₂, which were measured in the river water, were employed for the models. The performance of these models was assessed by the coefficient of determination R², root mean square error and mean absolute error. The results showed that the computed values of DO, BOD and COD using both the artificial neural network and ANFIS models were in close agreement with their respective measured values in the river water. MLP was also better than other models in predicting water quality variables. Finally, the sensitive analysis was done to determine the relative importance and contribution of the input variables. The results showed that the phosphate was the most effective parameters on DO, BOD and COD.