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This research was an effort to develop hybrid multilayer perceptron and radial basis function artificial neural network–genetic algorithm (MLPANN-GA and RBFANN-GA) models to accurately predict effluent biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in a submerged membrane bioreactor. The input variables of the networks were influent BOD, influent COD, influent TN or influent TP, sludge retention time (SRT), mixed liquor suspended solid, membrane permeability, and transmembrane pressure. Training procedures of all effluent quality parameters were successful for both the MLPANN-GA and RBFANN-GA models. The training and testing models showed an almost perfect match between the experimental and predicted values. Based upon the statistical analysis, results indicated that there is a very little difference between predicted and …