Predicting of soil contamination degree based on Support Vector Machine and K-Nearest Neighbor methods (A case study in Arak city, Iran)

Document Type: Original Article

Author

arak university of technology

Abstract

The contamination degree of soil of an urban region can change by heavy metals. This might result in endangering safety an urban region. This paper presents an approach to build a prediction model for the assessment of contamination degree index, based upon heavy metals changed. The heavy metal concentration of Pb, Cu, Ni, Zn, As, Cr and Ni as input was used to build a prediction model for the assessment of contamination degree. Two prediction models were implemented, support vector regression (SVR) and k-nearest neighbor regression method (KNNR).A comparison was made between these two models and the results show the superiority of the SVR model. Furthermore, a case study in Arak city, Iran was carried out to illustrate the capability of the support vector machines (SVM) model defined.

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