Integrating stratified best–worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)

MakaleAçık ErişimScopus SJRQ1
  • SDG 11 · Sürdürülebilir Şehirler ve Topluluklar

Özet

Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best–worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model’s applicability, a case study was conducted for the Erzurum region, one of Turkey’s landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study’s robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600–2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/munzur/2117Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s11356-023-30200-9Yayıncı resmi DOI adresi
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19
WoS
17
OpenAlex
18
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AlanDeğerDil
dc.contributor.authorKonurhan, Zekeriya
dc.contributor.authorYucesan, Melih
dc.contributor.authorGul, Muhammet
dc.date.accessioned2026-10-28T14:54:57.482059739Z
dc.date.available2026-10-28T14:54:57.482059739Z
dc.date.issued2023
dc.description.abstractLandslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best–worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model’s applicability, a case study was conducted for the Erzurum region, one of Turkey’s landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study’s robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600–2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.
dc.identifier.doi10.1007/s11356-023-30200-9
dc.identifier.eissn1614-7499
dc.identifier.endpage114000
dc.identifier.issn0944-1344
dc.identifier.issue53
dc.identifier.pmid37858024
dc.identifier.scopus2-s2.0-85174550933
dc.identifier.startpage113978
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/2117
dc.identifier.volume30
dc.identifier.wosWOS:001085974000021
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Science and Pollution Research
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBest–worst method
dc.subjectErzurum
dc.subjectGIS
dc.subjectLandslide susceptibility
dc.subjectS-MCDM
dc.subjectStratification
dc.subject.classificationEnvironmental Sciences
dc.subject.classificationEnvironmental Sciences & Ecology
dc.subject.otherDisasters
dc.subject.otherGeographic Information Systems
dc.subject.otherHumans
dc.subject.otherLandslides
dc.subject.otherRisk Assessment
dc.subject.otherTurkey
dc.subject.otherdisaster
dc.subject.othergeographic information system
dc.subject.otherhuman
dc.subject.otherlandslide
dc.subject.otherprocedures
dc.subject.otherturkey (bird)
dc.titleIntegrating stratified best–worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)
dc.typeArticle
rdl.citation.openalex18
rdl.citation.scopus19
rdl.citation.wos17
rdl.departmentDepartment of Geography, Munzur University, Tunceli, Turkey
rdl.departmentDepartment of Emergency Aid and Disaster Management, Munzur University, Tunceli, Turkey
rdl.departmentSchool of Transportation and Logistics, Istanbul University, Istanbul, Turkey
rdl.oa.statusgreen
rdl.oa.urlhttps://www.researchsquare.com/article/rs-2244162/latest.pdf
rdl.scopus.quartileQ1
rdl.sdgSDG11
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.source.indexPUBMED
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)