Prediction of Degree of Soil Contamination Based on Support Vector Machine and K-Nearest Neighbor Methods: A Case Study in Arak, Iran

The degree of soil contamination in an urban region can be changed by heavy metals. This mightresult in endangering safety of an urban region. This paper presents an approach to build a prediction modelfor the assessment of degree of contamination index, based upon heavy metals changes. The heavy metalconcentration of Pb, Cu, Ni, Zn, As, Cr and Ni as input was used to build a prediction model for the assessmentof degree of contamination. Two prediction models were implemented such as support vector regression (SVR)and k-nearest neighbor regression method (KNNR). A comparison was made between these two models andthe results showed the superiority of the SVR model. Furthermore, a case study in Arak, Iran was conductedto illustrate the capability of the support vector machines (SVM) model.