In India, river qualities are getting degraded diurnally. Actually, it is due to dumping of waste, discharging of untreated water and industrial waste etc. in to the river. Consequently, the river gets contaminated due to such anthropogenic activities. In this regard, Water quality index (WQI) is widely utilized to monitor the quality of river. WQI is a single unique number which represent the quality status of river water. In this study, a marking system based WQI has been developed for Chambal River. In which, the permissible limits (mentioned in IS-Code) of each water quality parameters have been utilized for developing sub-Indices. Subsequently, an average operator has been applied to agglomerate the sub-indices in to a single number termed as WQI. To achieve this, water quality parameter data has been imported from Central pollution control board database that composed the concentration of Bio-Chemical Oxygen demand (B.O.D), Dissolved Oxygen (D.O.), Conductivity, pH, Nitrate, Total Coliform, and Faecal Coliform of 10 different location of Chambal River. Additionally, a prediction model also has been developed by using Artificial Neural Network with artificial dataset. An artificial dataset was generated by utilizing the actual dataset with random sampling. In this direction, Levenberg Marquardt (LM), Baysian Regularization (BR), and Scaled Conjugate Gradient (SCG) algorithms were trained and tested with different settings of hyper-parameter. As a result,

Keywords : Water quality index (WQI), Artificial Neural Network (ANN), Chambal River.