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dc.creatorMarjanović, Miloš
dc.creatorKovačević, Miloš
dc.creatorBajat, Branislav
dc.creatorVozenilek, Vit
dc.date.accessioned2019-04-19T14:16:27Z
dc.date.available2019-04-19T14:16:27Z
dc.date.issued2011
dc.identifier.issn0013-7952
dc.identifier.urihttp://grafar.grf.bg.ac.rs/handle/123456789/356
dc.description.abstractThis paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.) attributes and one referent landslide inventory map from which to devise the classification rules. Three different machine learning algorithms were compared: Support Vector Machines, Decision Trees and Logistic Regression. A specific area of the Fruska Gora Mountain (Serbia) was selected to perform the entire modeling procedure, from attribute and referent data preparation/processing, through the classifiers' implementation to the evaluation, carried out in terms of the model's performance and agreement with the referent data. The experiments showed that Support Vector Machines outperformed the other proposed methods, and hence this algorithm was selected as the model of choice to be compared with a common knowledge-driven method - the Analytical Hierarchy Process - to create a landslide susceptibility map of the relevant area. The SVM classifier outperformed the AHP approach in all evaluation metrics (kappa index, area under ROC curve and false positive rate in stable ground class).en
dc.relationCzech Science Foundation 205/09/079
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/47014/RS//
dc.rightsrestrictedAccess
dc.sourceEngineering Geology
dc.subjectLandslide susceptibilityen
dc.subjectSupport Vector Machinesen
dc.subjectDecision Treeen
dc.subjectLogistic Regressionen
dc.subjectAnalytical Hierarchy Processen
dc.subjectClassificationen
dc.titleLandslide susceptibility assessment using SVM machine learning algorithmen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage234
dc.citation.issue3
dc.citation.other123(3): 225-234
dc.citation.rankM21
dc.citation.spage225
dc.citation.volume123
dc.identifier.doi10.1016/j.enggeo.2011.09.006
dc.identifier.rcubconv_1565
dc.identifier.scopus2-s2.0-80054680799
dc.identifier.wos000297182200008
dc.type.versionpublishedVersion


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