Machine Learning Algorithm for Prediction of Heavy Metal Contamination in the Groundwater in the Arak Urban Area

This paper attempts to predict heavy metals (Pb, Zn and Cu) in the groundwater from Arak city, using support vector regression model(SVR) by taking major elements (HCO₃, SO₄) in the groundwater from Arak city. 150 data samples and several models were trained and tested using collected data to determine the optimum model in which each model involved two inputs and three outputs. This SVR model fit captures the prime idea of statistical learning theory in order to obtain a good forecasting of the dependence among the major elements in the city of Arak. Finally, on the basis of these numerical calculations using SVR model, from the experimental data, conclusions of this study are exposed. By comparison between the predicted and the measured data it indicates that SVR model has strong potential to estimation of the heavy metals in the groundwater with high degree of accuracy.