Avalanche Risk Analysis by a Combined Geographic Information System and Bayesian Best-Worst Method

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Özet

The formation of avalanches is related to the land structure, climatic conditions, and snow cover. It is usually seen in mountainous and sloping terrains without vegetation. In Turkey, especially in Eastern Anatolia and the Black Sea Region, which have high elevations, avalanche events are observed. This study aims to perform a risk analysis by integrating the Bayesian Best-Worst method (BWM) and Geographic Information System (GIS) for Tunceli province, which is the scene of significant avalanche events. Bayesian BWM is a method that improves the original BWM by effectively integrating the preferences of multiple experts. In the study, 16 sub-criteria, such as elevation, slope, and the number of snowy days, were determined, and experts evaluated these criteria through questionnaires created. The weight of each criterion were calculated using the Bayesian-BWM. By integrating the criteria weights from the Bayesian-BWM model into GIS, the risky places for natural avalanche disasters in Tunceli province were determined, according to which the risk in the northern part of the study area is identified as high.

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dc.contributor.authorKonurhan, Zekeriya
dc.contributor.authorYücesan, Melih
dc.contributor.authorGul, Muhammet
dc.date.accessioned2026-10-28T14:55:43.433296836Z
dc.date.available2026-10-28T14:55:43.433296836Z
dc.date.issued2023
dc.description.abstractThe formation of avalanches is related to the land structure, climatic conditions, and snow cover. It is usually seen in mountainous and sloping terrains without vegetation. In Turkey, especially in Eastern Anatolia and the Black Sea Region, which have high elevations, avalanche events are observed. This study aims to perform a risk analysis by integrating the Bayesian Best-Worst method (BWM) and Geographic Information System (GIS) for Tunceli province, which is the scene of significant avalanche events. Bayesian BWM is a method that improves the original BWM by effectively integrating the preferences of multiple experts. In the study, 16 sub-criteria, such as elevation, slope, and the number of snowy days, were determined, and experts evaluated these criteria through questionnaires created. The weight of each criterion were calculated using the Bayesian-BWM. By integrating the criteria weights from the Bayesian-BWM model into GIS, the risky places for natural avalanche disasters in Tunceli province were determined, according to which the risk in the northern part of the study area is identified as high.
dc.identifier.doi10.1007/978-3-031-40328-6_11
dc.identifier.endpage210
dc.identifier.issn2731-040X
dc.identifier.scopus2-s2.0-85205090348
dc.identifier.startpage193
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/2716
dc.identifier.volumePart F3796
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.ispartofLecture Notes in Operations Research
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAvalanche risk analysis
dc.subjectBayesian-BWM
dc.subjectGIS
dc.titleAvalanche Risk Analysis by a Combined Geographic Information System and Bayesian Best-Worst Method
dc.typeBook Chapter
rdl.citation.scopus5
rdl.oa.statusclosed
rdl.source.indexSCOPUS