Medium scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey

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  • SDG 11 · Sürdürülebilir Şehirler ve Topluluklar

Özet

The aim of the present study was to produce an earthflow susceptibility map for the city center and environs of the province of Rize located at the Northeastern part of Turkey. The study area is the rainiest region of Turkey, and due to the triggering effect of precipitation earthflows are frequently observed in and around the study area. Besides this point, weathered rock units and steep topography accompany with precipitation for the occurrence of earthflow cases. Considering this point, an earthflow susceptibility mapping was inferred to be a necessity for the area. Parameters, such as lithology, slope gradient, slope aspect, topographical wetness index (TWI), stream power index (SPI), slope curvature, and sediment capacity index (LS) were considered to be earthflow conditioning parameters. A multi-temporal earthflow inventory map for a period of 4 years (2011–2015) was initially generated by way of remote sensing approach and field surveys. Earthflow susceptibility map was produced using the logistic regression method after which the produced susceptibility map was validated. The mapped earthflows were separated into two groups prior to modelling and validation. The first group was for training and the second group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of mapped earthflows. Area under curvature (AUC) approach was applied for validation purposes. The prediction capability of the earthflow susceptibility map produced can be regarded as acceptable in accordance with the AUC values of 0.62 for the logistic regression model. Based on these results, the obtained earthflow susceptibility map can be used to mitigate hazards related to landslides and to aid in land-use planning for the Rize city center.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/gumushane/12891Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s12665-020-09217-7Yayıncı resmi DOI adresi
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26
WoS
23
OpenAlex
31
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Tam Kayıt
AlanDeğerDil
dc.contributor.authorDağ, Serhat
dc.contributor.authorAkgün, Aykut
dc.contributor.authorKaya, Ayberk
dc.contributor.authorAlemdağ, Selçuk
dc.contributor.authorBostancı, Hasan Tahsin
dc.date.accessioned2026-10-22T10:20:40.329724480Z
dc.date.available2026-10-22T10:20:40.329724480Z
dc.date.issued2020
dc.description.abstractThe aim of the present study was to produce an earthflow susceptibility map for the city center and environs of the province of Rize located at the Northeastern part of Turkey. The study area is the rainiest region of Turkey, and due to the triggering effect of precipitation earthflows are frequently observed in and around the study area. Besides this point, weathered rock units and steep topography accompany with precipitation for the occurrence of earthflow cases. Considering this point, an earthflow susceptibility mapping was inferred to be a necessity for the area. Parameters, such as lithology, slope gradient, slope aspect, topographical wetness index (TWI), stream power index (SPI), slope curvature, and sediment capacity index (LS) were considered to be earthflow conditioning parameters. A multi-temporal earthflow inventory map for a period of 4 years (2011–2015) was initially generated by way of remote sensing approach and field surveys. Earthflow susceptibility map was produced using the logistic regression method after which the produced susceptibility map was validated. The mapped earthflows were separated into two groups prior to modelling and validation. The first group was for training and the second group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of mapped earthflows. Area under curvature (AUC) approach was applied for validation purposes. The prediction capability of the earthflow susceptibility map produced can be regarded as acceptable in accordance with the AUC values of 0.62 for the logistic regression model. Based on these results, the obtained earthflow susceptibility map can be used to mitigate hazards related to landslides and to aid in land-use planning for the Rize city center.
dc.description.sponsorshipRecep Tayyip Erdogan Üniversitesi, RTEU, (2014.109.01.01)
dc.identifier.articlenumber468
dc.identifier.doi10.1007/s12665-020-09217-7
dc.identifier.eissn1866-6299
dc.identifier.issn1866-6280
dc.identifier.issue19
dc.identifier.scopus2-s2.0-85091988939
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/gumushane/12891
dc.identifier.volume79
dc.identifier.wosWOS:000576915800002
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofEnvironmental Earth Sciences
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEarthflow
dc.subjectLogistic regression
dc.subjectRemote sensing
dc.subjectSusceptibility
dc.subjectTurkey
dc.subject.classificationEnvironmental Sciences
dc.subject.classificationGeosciences, Multidisciplinary
dc.subject.classificationWater Resources
dc.subject.classificationEnvironmental Sciences & Ecology
dc.subject.classificationGeology
dc.subject.otherRize [Turkey]
dc.subject.otherTurkey
dc.subject.otherGeographic information systems
dc.subject.otherLand use
dc.subject.otherLandslides
dc.subject.otherLithology
dc.subject.otherLogistic regression
dc.subject.otherMultivariant analysis
dc.subject.otherTopography
dc.subject.otherLogistic regression method
dc.subject.otherLogistic Regression modeling
dc.subject.otherModelling and validation
dc.subject.otherMultivariate statistics
dc.subject.otherPrediction capability
dc.subject.otherRemote sensing approaches
dc.subject.otherStream power index
dc.subject.otherSusceptibility mapping
dc.subject.otherGIS
dc.subject.otherlandslide
dc.subject.othermodel validation
dc.subject.otherprecipitation (climatology)
dc.subject.otherregression analysis
dc.subject.otherremote sensing
dc.subject.othertraining
dc.titleMedium scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey
dc.typeArticle
rdl.citation.openalex31
rdl.citation.scopus26
rdl.citation.wos23
rdl.oa.statusclosed
rdl.scopus.quartileQ2
rdl.sdgSDG11
rdl.sdgSDG06
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)