Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

This paper examined the efficiency of multivariate linear regression (mlr) and artificial neural network (ann)models in prediction of two major water quality parameters in a wastewater treatment plant. biochemical oxygendemand (bod) and chemical oxygen demand (cod) as well as indirect indicators of organic matters arerepresentative parameters for sewer water quality. performance of the ann models was evaluated using coefficientof correlation (r), root mean square error (rmse) and bias values. the computed values of bod and cod by model,ann method and regression analysis were in close agreement with their respective measured values. resultsshowed that the ann performance model was better than the mlr model. comparative indices of the optimizedann with input values of temperature (t), ph, total suspended solid (tss) and total suspended (ts) for prediction ofbod was rmse = 25.1 mg/l, r = 0.83 and for prediction of cod was rmse = 49.4 mg/l, r = 0.81. it was found thatthe ann model could be employed successfully in estimating the bod and cod in the inlet of wastewaterbiochemical treatment plants. moreover, sensitive examination results showed that ph parameter have more effecton bod and cod predicting to another parameters. also, both implemented models have predicted bod betterthan cod.