Water Quality Modelling of Mahi Basin Using Neural Network and ANFIS

Abstract

Dissolved oxygen (DO) Biochemical Oxygen demand (BOD)concentrations have been used as primary indicator of
stream water quality. A problem of great social importance is determining how to best retain the quality of stream water and
maintain DO and BOD concentrations using various pollution control activities. Application of ANFIS technique is used to
estimate BOD and DO concentrations at upstreme and downstream of Mahi river basin , Monthly data sets on temperature,
pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD) and dissolved oxygen (DO) at three locations,
namely, Mataji (upper basin), paderdibadi (upper basin) and Khanpur (lower basin) have been used for the analysis. The
performance of the ANFIS models was assessed through the correlation coefficient (R), mean squared error (MSE), mean
absolute error (MAE) . Study results show that the adaptive neuro-fuzzy inference system is able to predict the biochemical
oxygen demand and dissolved oxygen with reasonable accuracy, suggesting that the ANFIS model is a valuable tool for river
water quality estimation.. The results also suggest that ANFIS method can be successfully applied to establish river water quality
prediction model