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dc.contributor.authorYilmaz, Isik
dc.date.accessioned2019-07-27T12:10:23Z
dc.date.accessioned2019-07-28T10:07:31Z
dc.date.available2019-07-27T12:10:23Z
dc.date.available2019-07-28T10:07:31Z
dc.date.issued2010
dc.identifier.issn1866-6280
dc.identifier.urihttps://dx.doi.org/10.1007/s12665-009-0394-9
dc.identifier.urihttps://hdl.handle.net/20.500.12418/9814
dc.descriptionWOS: 000280385900014en_US
dc.description.abstractThis case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.en_US
dc.language.isoengen_US
dc.publisherSPRINGERen_US
dc.relation.isversionof10.1007/s12665-009-0394-9en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLandslideen_US
dc.subjectSusceptibility mapen_US
dc.subjectGISen_US
dc.subjectConditional probabilityen_US
dc.subjectLogistic regressionen_US
dc.subjectArtificial neural networksen_US
dc.subjectSupport vector machineen_US
dc.titleComparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machineen_US
dc.typearticleen_US
dc.relation.journalENVIRONMENTAL EARTH SCIENCESen_US
dc.contributor.departmentCumhuriyet Univ, Fac Engn, Dept Geol Engn, TR-58140 Sivas, Turkeyen_US
dc.identifier.volume61en_US
dc.identifier.issue4en_US
dc.identifier.endpage836en_US
dc.identifier.startpage821en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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