prikaz prve stranice dokumenta Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods
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Scientific paper - Original scientific paper
Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods
Geodetski list, 74 (97) (2020), 4; 357-372. urn:nbn:hr:151:618711
Radočaj, Dorijan; Jurišić, Mladen; Župan, Robert; Antonić, Oleg

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Radočaj, D., Jurišić, M., Župan, R. & Antonić, O. (2020). Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods. Geodetski list, 74 (97). (4), 357-372. Retrieved from https://urn.nsk.hr/urn:nbn:hr:151:618711

Radočaj, Dorijan, et al. "Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods." Geodetski list, vol. 74 (97), no. 4, 2020, pp. 357-372. https://urn.nsk.hr/urn:nbn:hr:151:618711

Radočaj, Dorijan, Mladen Jurišić, Robert Župan and Oleg Antonić. "Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods." Geodetski list 74 (97), no. 4 (2020): 357-372. https://urn.nsk.hr/urn:nbn:hr:151:618711

Radočaj, D., et al. (2020) 'Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods', Geodetski list, 74 (97)(4), pp. 357-372. Available at: https://urn.nsk.hr/urn:nbn:hr:151:618711 (Accessed 02 March 2025)

Radočaj D, Jurišić M, Župan R, Antonić O. Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods. Geodetski list [Internet]. 2020 December 31 [cited 2025 March 02];74 (97)(4):357-372. Available at: https://urn.nsk.hr/urn:nbn:hr:151:618711

D. Radočaj, M. Jurišić, R. Župan and O. Antonić, "Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods", Geodetski list, vol. 74 (97), no. 4, pp. 357-372, December 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:151:618711. [Accessed: 02 March 2025]

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Metadata
Title (english)Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods
AuthorDorijan Radočaj
AuthorMladen Jurišić
AuthorRobert Župan
AuthorOleg Antonić
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Agrobiotechnical Sciences Osijek
(Department of Agricultural Engineering and Renewable Energy Resources)
Scientific / art field,
discipline and subdiscipline		BIOTECHNICAL SCIENCES
Agronomy
Agricultural Engineering and Technology
Abstract (english)
Soil contamination caused by heavy metals presents a potential long-term issue to human health and biodiversity due to the bioaccumulation effect. Previous research at the micro level in Croatia detected soil contamination caused by heavy metals above maximum permitted values, which also implied the necessity of their current spatial representation at the macro level in Croatia. The aim of this study was to provide a spatial prediction of six heavy metals considered as contaminants of soils in continental Croatia using two approaches: a conventional approach based on interpolation and a machine learning approach. The prediction was performed on the most recent available data on cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) concentrations in soils, from the Ministry of environment and energy. The conventional prediction approach consisted of the interpolation using the ordinary kriging (OK) in case of input data normality and stationarity, alongside the inverse distance weighting (IDW) method. For the machine learning approach, random forest (RF) and support vector machine (SVM) methods were used. IDW outperformed RF and SVM prediction results for all soil heavy metals contents, primarily due to sparse soil sampling. Soil Cr contents were predicted above the maximum allowed limit, while elevated soil contamination levels in some parts of the study area were detected for Ni and Zn. The highest soil contamination levels were observed in the urban areas of generalized land cover classes, indicating a necessity for its monitoring and the adjustment of land-use management plans.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - domestic
Publication versionPublished version
Journal titleGeodetski list
Numberingvol. 74 (97), no. 4, pp. 357-372
p-ISSN0016-710X
e-ISSN1849-0611
URN:NBNurn:nbn:hr:151:618711
Publication2020-12-31
Document URLhttps://hrcak.srce.hr/251228
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-06-26 14:41:04