TY  - JOUR
AU  - Dragović, Snežana
AU  - Cujić, Mirjana
AU  - Slavković-Beskoski, Latinka
AU  - Gajić, Boško
AU  - Bajat, Branislav
AU  - Kilibarda, Milan
AU  - Onjia, Antonije E.
PY  - 2013
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/3273
AB  - The content of trace elements (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn) in surface soils in the area surrounding the largest coal-fired power plant in Serbia was determined to assess the contribution of emissions to pollution. Analysis of mutual associations between the trace elements and their correlation with soil particle size fractions indicated anthropogenic origin at most sampling sites. Enrichment factor analysis confirmed these findings. Common patterns in trace element concentrations of the analysed soils were identified by hierarchical cluster analysis. Explanatory spatial analysis, used for characterization and mapping of spatial variability patterns, revealed the highest concentrations of trace elements in areas in predominant wind directions.
PB  - Elsevier Science Bv, Amsterdam
T2  - Catena
T1  - Trace element distribution in surface soils from a coal burning power production area: A case study from the largest power plant site in Serbia
EP  - 296
SP  - 288
VL  - 104
DO  - 10.1016/j.catena.2012.12.004
ER  - 

@article{
author = "Dragović, Snežana and Cujić, Mirjana and Slavković-Beskoski, Latinka and Gajić, Boško and Bajat, Branislav and Kilibarda, Milan and Onjia, Antonije E.",
year = "2013",
abstract = "The content of trace elements (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn) in surface soils in the area surrounding the largest coal-fired power plant in Serbia was determined to assess the contribution of emissions to pollution. Analysis of mutual associations between the trace elements and their correlation with soil particle size fractions indicated anthropogenic origin at most sampling sites. Enrichment factor analysis confirmed these findings. Common patterns in trace element concentrations of the analysed soils were identified by hierarchical cluster analysis. Explanatory spatial analysis, used for characterization and mapping of spatial variability patterns, revealed the highest concentrations of trace elements in areas in predominant wind directions.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Catena",
title = "Trace element distribution in surface soils from a coal burning power production area: A case study from the largest power plant site in Serbia",
pages = "296-288",
volume = "104",
doi = "10.1016/j.catena.2012.12.004"
}

Dragović, S., Cujić, M., Slavković-Beskoski, L., Gajić, B., Bajat, B., Kilibarda, M.,& Onjia, A. E.. (2013). Trace element distribution in surface soils from a coal burning power production area: A case study from the largest power plant site in Serbia. in Catena
Elsevier Science Bv, Amsterdam., 104, 288-296.
https://doi.org/10.1016/j.catena.2012.12.004

Dragović S, Cujić M, Slavković-Beskoski L, Gajić B, Bajat B, Kilibarda M, Onjia AE. Trace element distribution in surface soils from a coal burning power production area: A case study from the largest power plant site in Serbia. in Catena. 2013;104:288-296.
doi:10.1016/j.catena.2012.12.004 .

Dragović, Snežana, Cujić, Mirjana, Slavković-Beskoski, Latinka, Gajić, Boško, Bajat, Branislav, Kilibarda, Milan, Onjia, Antonije E., "Trace element distribution in surface soils from a coal burning power production area: A case study from the largest power plant site in Serbia" in Catena, 104 (2013):288-296,
https://doi.org/10.1016/j.catena.2012.12.004 . .

TY  - JOUR
AU  - Kovacević, Milos
AU  - Bajat, Branislav
AU  - Gajić, Boško
PY  - 2010
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/2363
AB  - Quantitative techniques for prediction and classification in soil survey are developing rapidly. The paper introduces application of Support Vector Machines in the estimate of values of soil properties and soil type classification based on known values of particular chemical and physical properties in sampled profiles. Comparison of proposed approach with other linear regression models shows that Support Vector Machines are the model of choice for estimation of values of physical properties and pH value when using only chemical data inputs. They are also the model of choice in the cases where chemical data inputs are not strongly correlated to the estimated property. However, in classification task, their performance is similar to that of the other compared methods, with an increasing advantage when a data set consists of a small number of training samples per each soil type.
PB  - Elsevier, Amsterdam
T2  - Geoderma
T1  - Soil type classification and estimation of soil properties using support vector machines
EP  - 347
IS  - 3-4
SP  - 340
VL  - 154
DO  - 10.1016/j.geoderma.2009.11.005
ER  - 

@article{
author = "Kovacević, Milos and Bajat, Branislav and Gajić, Boško",
year = "2010",
abstract = "Quantitative techniques for prediction and classification in soil survey are developing rapidly. The paper introduces application of Support Vector Machines in the estimate of values of soil properties and soil type classification based on known values of particular chemical and physical properties in sampled profiles. Comparison of proposed approach with other linear regression models shows that Support Vector Machines are the model of choice for estimation of values of physical properties and pH value when using only chemical data inputs. They are also the model of choice in the cases where chemical data inputs are not strongly correlated to the estimated property. However, in classification task, their performance is similar to that of the other compared methods, with an increasing advantage when a data set consists of a small number of training samples per each soil type.",
publisher = "Elsevier, Amsterdam",
journal = "Geoderma",
title = "Soil type classification and estimation of soil properties using support vector machines",
pages = "347-340",
number = "3-4",
volume = "154",
doi = "10.1016/j.geoderma.2009.11.005"
}

Kovacević, M., Bajat, B.,& Gajić, B.. (2010). Soil type classification and estimation of soil properties using support vector machines. in Geoderma
Elsevier, Amsterdam., 154(3-4), 340-347.
https://doi.org/10.1016/j.geoderma.2009.11.005

Kovacević M, Bajat B, Gajić B. Soil type classification and estimation of soil properties using support vector machines. in Geoderma. 2010;154(3-4):340-347.
doi:10.1016/j.geoderma.2009.11.005 .

Kovacević, Milos, Bajat, Branislav, Gajić, Boško, "Soil type classification and estimation of soil properties using support vector machines" in Geoderma, 154, no. 3-4 (2010):340-347,
https://doi.org/10.1016/j.geoderma.2009.11.005 . .