Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River

Abstract This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River of Bangladesh. The data sets consist of 10 water quality parameters which include pH, alkalinity (mg/L as CaCO 3 ), hardness, total solids (TS), total dissolved solids (TDS), potassium (K + ), PO 4 −3 (mg/l), NO 3 − (mg/l), BOD (mg/l) and DO (mg/l). The performance of the ANFIS models was assessed through the correlation coefficient ( R ), mean squared error (MSE), mean absolute error (MAE) and Nash model efficiency ( E ). Study results show that the adaptive neuro-fuzzy inference system is able to predict the biochemical oxygen demand with reasonable accuracy, suggesting that the ANFIS model is a valuable tool for river water quality estimation. The result shows that, ANFIS-I has a high prediction capacity of BOD compared with ANFIS-II. The results also suggest that ANFIS method can be successfully applied to establish river water quality prediction model.