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dc.contributor.authorOkwuashi, O
dc.contributor.authorNdehedehe, C
dc.date.accessioned2018-07-18T02:27:10Z
dc.date.available2018-07-18T02:27:10Z
dc.date.issued2015
dc.identifier.issn1996-7489
dc.identifier.doi10.17159/sajs.2015/20140153
dc.identifier.urihttp://hdl.handle.net/10072/378538
dc.description.abstractDigital terrain model interpolation is intrinsically a surface fitting problem, in which unknown heights H are estimated from known X-Y coordinates. Notable methods of digital terrain model interpolation include inverse distance to power, local polynomial, minimum curvature, modified Shepard’s method, nearest neighbour and polynomial regression. We investigated the support vector machine regression (SVMR) as a new alternative method to these models. SVMR is a contemporary machine learning algorithm that has been applied to several real-world problems aside from digital terrain modelling. The SVMR results were compared with those from notable parametric (the nearest neighbour) and non-parametric (the artificial neural network) techniques. Four categories of error analysis were used to assess the accuracy of the modelling: minimum error, maximum error, means error and standard error. The results indicate that SVMR furnished the lowest error, followed by the artificial neural network model. The SVMR also produced the smoothest surface followed by the artificial neural network model. The high accuracy furnished by SVMR in this experiment attests that SVMR is a promising model for digital terrain model interpolation.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAcademy of Science of South Africa
dc.relation.ispartofpagefrom2014-0153-1
dc.relation.ispartofpageto2014-0153-5
dc.relation.ispartofissue9/10
dc.relation.ispartofjournalSouth African Journal of Science
dc.relation.ispartofvolume111
dc.subject.fieldofresearchGeospatial Information Systems
dc.subject.fieldofresearchcode090903
dc.titleDigital terrain model height estimation using support vector machine regression
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorNdehedehe, Christopher E.


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