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Landslide risk zoning using support vector machine algorithm

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dc.citedby1
dc.contributor.authorGhiasi V.en_US
dc.contributor.authorPauzi N.I.M.en_US
dc.contributor.authorKarimi S.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorid26535838400en_US
dc.contributor.authorid26536518100en_US
dc.contributor.authorid58551577200en_US
dc.contributor.authorid54880470900en_US
dc.date.accessioned2024-10-14T03:20:47Z
dc.date.available2024-10-14T03:20:47Z
dc.date.issued2023
dc.description.abstractLandslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curveen_US
dc.description.abstractthe root means square error, and the correlation coefficient. Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed. � 2023 Techno-Press, Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.12989/gae.2023.34.3.267
dc.identifier.epage284
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85168873351
dc.identifier.spage267
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85168873351&doi=10.12989%2fgae.2023.34.3.267&partnerID=40&md5=10527569130696c83e9992ee17ebe720
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34576
dc.identifier.volume34
dc.pagecount17
dc.publisherTechno-Pressen_US
dc.sourceScopus
dc.sourcetitleGeomechanics and Engineering
dc.subjectkernel functions
dc.subjectlandslide
dc.subjectlogistic function
dc.subjectmapping
dc.subjectprediction rate-area diagram
dc.subjectsupport vector machine
dc.subjectDisasters
dc.subjectLosses
dc.subjectRisk assessment
dc.subjectSupport vector machines
dc.subjectTopography
dc.subjectVectors
dc.subjectWatersheds
dc.subjectFinancial loss
dc.subjectGaussian kernels
dc.subjectKernel function
dc.subjectLandslide risk zoning
dc.subjectLogistics functions
dc.subjectNatural disasters
dc.subjectPrediction rate-area diagram
dc.subjectPrediction-rates
dc.subjectSupport vector machines algorithms
dc.subjectSupport vectors machine
dc.subjectLandslides
dc.titleLandslide risk zoning using support vector machine algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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