prikaz prve stranice dokumenta Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods
  Preuzmi
PDF 2.57 MB
Znanstveni rad - Izvorni znanstveni rad
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

Citirajte ovaj rad

Radočaj, D., Jurišić, M., Župan, R. i 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. Preuzeto s 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), br. 4, 2020, str. 357-372. https://urn.nsk.hr/urn:nbn:hr:151:618711

Radočaj, Dorijan, Mladen Jurišić, Robert Župan i Oleg Antonić. "Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods." Geodetski list 74 (97), br. 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), str. 357-372. Preuzeto s: https://urn.nsk.hr/urn:nbn:hr:151:618711 (Datum pristupa: 23.11.2024.)

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]. 31.12.2020. [pristupljeno 23.11.2024.];74 (97)(4):357-372. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:151:618711

D. Radočaj, M. Jurišić, R. Župan i O. Antonić, "Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods", Geodetski list, vol. 74 (97), br. 4, str. 357-372, Prosinac 2020. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:151:618711. [Citirano: 23.11.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:151:618711
Prijavite se u repozitorij kako biste mogli spremiti objekt u svoju listu.
Podaci o radu
Naslov (engleski)Spatial Prediction of Heavy Metal Soil Contents in Continental Croatia Comparing Machine Learning and Spatial Interpolation Methods
AutorDorijan Radočaj
AutorMladen Jurišić
AutorRobert Župan
AutorOleg Antonić
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Fakultet agrobiotehničkih znanosti Osijek
(Zavod za poljoprivrednu tehniku i obnovljive izvore energije)
Znanstveno / umjetničko
područje, polje i grana		BIOTEHNIČKE ZNANOSTI
Poljoprivreda (agronomija)
Poljoprivredna tehnika i tehnologija
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - domaća recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaGeodetski list
Brojčani podacivol. 74 (97), br. 4, str. 357-372
p-ISSN0016-710X
e-ISSN1849-0611
URN:NBNurn:nbn:hr:151:618711
Datum objave publikacije2020-12-31
URL dokumentahttps://hrcak.srce.hr/251228
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2024-06-26 14:41:04