Original scientific paper

https://doi.org/10.17559/TV-20140807111130

Prediction of magnetic susceptibility class of soil using decision trees

Meltem Kurt ; University of Kocaeli, Department of Computer Engineering, Faculty of Engineering, Umuttepe Campus, 41380 Izmit Kocaeli, Turkey
Nevcihan Duru ; University of Kocaeli, Department of Computer Engineering, Faculty of Engineering, Umuttepe Campus, 41380 Izmit Kocaeli, Turkey
M. Mucella Canbay ; University of Kocaeli, Department of Geophysics, Faculty of Engineering, Umuttepe Campus, 41380 Izmit Kocaeli, Turkey
H. Tarik Duru orcid.org/0000-0001-9887-8169 ; University of Kocaeli, Department of Electrical Engineering, Faculty of Engineering, Umuttepe Campus, 41380 Izmit Kocaeli, Turkey

Abstract

Magnetic susceptibility (MS) is a dimensionless proportionality constant that indicates the degree of magnetization of a material in response to an applied magnetic field. In our study, the focus is to predict the magnetic susceptibility classification of the soil by using data mining algorithms. Magnetic susceptibility values depend on the composition, grain size of magnetic minerals and their source, such as lithogenic, pedogenic and anthropogenic origins. In this paper, we applied two data mining classification algorithms which are called ID3 and C4.5 for predicting MS class and the degree of pollution along the Izmir area in Turkey. By applying the algorithms, possible MS classes are obtained, according to the heavy metal concentration (Pb, Cu, Zn, Co, Cd, Ni) values related to MS. The aim of applying the algorithms is constructing the decision tree and the rules so as to obtain MS values. Thus, errors resulting from the change of ambient conditions and the measurement difficulties are eliminated. According to the rules, we reached 82 % accuracy condition and it is shown that test values and the measurement values are compatible with each other.

Keywords

data mining; classification; heavy metal contamination; magnetic susceptibility

Hrčak ID:

153159

URI

https://hrcak.srce.hr/153159

Publication date:

19.2.2016.

Article data in other languages: croatian

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