Prediction of wastewater treatment plant performance using multilinear regression and artificial neural networks

In this study, modeling of Konya wastewater treatment plant was studied by using multilinear regression and artificial neural network with different architectures in SPSS and MATLAB software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account the input values of pH, temperature, COD, TSS and BOD with output values of COD. To compare the performance of the model, coefficient of determination (R²) and Mean Squared Error (MSE) were used. In Multilinear regression method, to understand the effects of the tested parameters, regression function was developed. The highest prediction efficiencies was obtained two hidden layers in Artificial Neural Network models. According to the modeling study, Artificial Neural Network models responded more satisfactory results than Multilinear Regression model.