Show simple item record

dc.creatorKovačević, Miloš
dc.creatorBajat, Branislav
dc.creatorGajić, Boško
dc.date.accessioned2019-04-19T14:15:01Z
dc.date.available2019-04-19T14:15:01Z
dc.date.issued2010
dc.identifier.issn0016-7061
dc.identifier.urihttps://grafar.grf.bg.ac.rs/handle/123456789/293
dc.description.abstractQuantitative 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.en
dc.rightsrestrictedAccess
dc.sourceGeoderma
dc.subjectSupport vector machinesen
dc.subjectClassificationen
dc.subjectRegressionen
dc.subjectSoil typesen
dc.subjectChemical propertiesen
dc.subjectPhysical propertiesen
dc.titleSoil type classification and estimation of soil properties using support vector machinesen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage347
dc.citation.issue3-4
dc.citation.other154(3-4): 340-347
dc.citation.rankM21
dc.citation.spage340
dc.citation.volume154
dc.identifier.doi10.1016/j.geoderma.2009.11.005
dc.identifier.rcubconv_1519
dc.identifier.scopus2-s2.0-72549110480
dc.identifier.wos000275009400022
dc.type.versionpublishedVersion


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record