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

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 CaCO3), hardness, total solids (TS), total dissolved solids (TDS), potassium (K+), PO4−3 (mg/l), NO3− (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. © 2015