Predicting Water Quality Parameters Using Machine Learning

The growing worldwide emphasis on dealing with water quality is giving rise to a widespread research and expanding market for novel and astute monitoring systems. The current method is laboratory process where samples are taken from water bodies and testing is done in labs. This method is time consuming, wastage of man power, and not economical. So, Artificial Neural Network(ANN) is used to solve this problem. This method eliminates chemical method of evaluating water quality parameters and also is cost effective. This paper gives brief methodology to predict unknown parameters such as Alkalinity, Chloride, Sulphate values using known parameters such as pH, Electrical Conductivity, TDS etc. using Levenberg-Marquardt algorithm, which helps in further classification of water bodies for different application. Results gave accuracy of 83.94%, 87.9%, 81.736%, 79.48% in predicting chloride, total-hardness, sulphate, total alkalinity respectively.