Ground Water Quality Index Prediction Using Random Forest Model

The present work predicts and assesses the water quality index (WQI) that exhibits overall water quality levels using machine learning. The physiochemical parameters taken into account for the present work for drinking water quality index are pH, calcium, magnesium, sulphate, chloride, nitrate, fluoride, total hardness, total alkalinity, iron and sodium in mg/l. The physiochemical parameters for irrigation water quality index are electrical conductivity, residual sodium carbonate and SAR in mg/l. WQI is predicted from Yearly Ground Water Quality information from 01 January 2000 to 01 January 2018 using Central Ground Water Board (CGWB) data of Jaipur in the state Rajasthan, India. The data contains information from 118 Ground Water Points /Stations in Ganga Basin. Furthermore, IS-10500 (June 2015) and IS:11624-1986 (Reaffirmed 2001) limits are used for the calculating WQI for drinking and irrigation purposes, respectively. Decision tree regressor and regression random forest models were used for predicting water quality index. Water quality index is determined by the ground water physiochemical parameters. Random forest model outperformed decision tree model by achieving higher model accuracy with RMSE 10.92 and MAE 7.16.