Modelling COD concentration by using three different ANFIS techniques

Abstract

Artificial intelligence (AI) techniques have been successfully performed in many different water resources applications such as rainfall-runoff, precipitation, evaporation, discharge (Q), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), sediment concentration and lake levels by many researchers over the last three decades. In this study, three different adaptive neuro-fuzzy inference system (ANFIS) techniques, ANFIS with fuzzy clustering (ANFIS-FCM), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), were developed to estimate COD concentration by using various combinations of daily input important variables water suspended solids (SS), discharge (Q), temperature (T) and pH. Root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics were used for the comparison criteria. Training, testing and validation phase’s results of the optimal ANFIS models were also graphically compared each other. Comparison of the results indicated that the ANFIS-SC(1,0.3,1) model whose input is water SS was found to be slightly better than the other models in estimation of COD according to the comparison criteria in testing phase. In the validation phase, however, ANFISFCM( 1,3,gauss,1) model performed slightly better than ANFIS-GP(3,trimf,constant,1) and ANFIS-SC(1,0.3,1) models. It can be said that three different ANFIS techniques provide similar accuracy in estimating COD.