Digital Soil Mapping (DSM) Using a GIS-Based RF Machine Learning Model: The Case of Strandzha Mountains (Thrace Peninsula, Türkiye)

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dc.contributor.authorOzsahin, Emre
dc.contributor.authorSari, Huseyin
dc.contributor.authorErdem, Duygu Boyraz
dc.contributor.authorOzturk, Mikayil
dc.date.accessioned2024-10-29T17:59:10Z
dc.date.available2024-10-29T17:59:10Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThis study assessed and mapped the spatial distribution of soil types and properties developed under the forest cover of the Strandzha Mountains of T & uuml;rkiye. The study was conducted on a micro-scale in the riparian zone of the Balaban River, which characterizes the soils distributed in the mountainous area. The effect of environmental factors on the spatial distribution of soil types and properties was also determined. To gather data, soil sampling, laboratory analysis, data processing and mapping were sequentially performed. These data were analyzed using the Geographical Information System (GIS) based Random Forest (RF) machine learning technique. Digital Soil Mapping (DSM) was developed with satisfactory performance. DSM suggests that the factors affecting the spatial distribution of soil types and properties in the sample area are, from most important to least important, topography (50.77%), climate (28.14%), organisms (8.22%), parent material (7.24%) and time (5.63%). With the contributions of all these factors in different proportions, it was determined that soils belonging to the Entisol and then Inceptisol orders were the most widespread in the sample area. The study results revealed that the GIS-based RF machinelearning technique can be used as a reliable tool for the development of DSM in mountainous terrains.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Tekirdag Namimath;k Kemal University [NKUBAP.03.YLGA.21.324]
dc.description.sponsorshipThis study was funded by the Scientific Research Projects Coordination Unit of Tekirdag Nam & imath;k Kemal University. Project Number: NKUBAP.03.YLGA.21.324.
dc.identifier.doi10.32604/rig.2024.054197
dc.identifier.endpage361
dc.identifier.issn1260-5875
dc.identifier.issn2116-7060
dc.identifier.startpage341
dc.identifier.urihttps://doi.org/10.32604/rig.2024.054197
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14648
dc.identifier.volume33
dc.identifier.wosWOS:001325702400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherTech Science Press
dc.relation.ispartofRevue Internationale De Geomatique
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDSM
dc.subjectGIS
dc.subjectRF
dc.subjectsoil
dc.subjectStrandzha Mountains
dc.titleDigital Soil Mapping (DSM) Using a GIS-Based RF Machine Learning Model: The Case of Strandzha Mountains (Thrace Peninsula, Türkiye)
dc.typeArticle

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