A comparison of different machine learning models for landslide susceptibility mapping in Rize (Türkiye)

dc.contributor.authorBilgilioğlu, Hacer
dc.date.accessioned2024-04-18T12:49:37Z
dc.date.available2024-04-18T12:49:37Z
dc.date.issued2023
dc.departmentMühendislik Fakültesi
dc.description.abstractThe main purpose of this study was to compare the performance and validation of six machine learning models (extreme gradient boosting, random forest, artificial neural network, support vector machine, C4.5 decision tree, and naive Bayes) in landslide susceptibility modelling. The province of Rize, which has the highest rate of landslide events in Türkiye, was chosen as the study area. The conditioning factors (distance to roads, lithology, drainage density, slope, topographic wetness index (TWI), soil depth, distance to rivers, land use, NDVI, plan curvature, elevation, aspect, profile curvature) affecting the landslide were determined using the ReliefF method. A total of 516 landslides were identified for creating models, comparing performance, and validating results. The performance and validation of the models were determined by the receiver operating characteristics (ROC), sensitivity, specificity, accuracy, and kappa index. The results show that the XGBoost model outperforms the other five machine learning models in terms of accuracy and performance and is the most effective model for generating landslide susceptibility maps in Rize (Türkiye).
dc.identifier.doi10.5200/baltica.2023.2.3
dc.identifier.endpage132en_US
dc.identifier.issn0067-3064
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage115en_US
dc.identifier.urihttps:/dx.doi.org10.5200/baltica.2023.2.3
dc.identifier.urihttps://hdl.handle.net/20.500.12451/11645
dc.identifier.volume36en_US
dc.identifier.wosWOS:001121694100002
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNature Research Centre
dc.relation.ispartofBaltica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLandslide
dc.subjectMachine Learning
dc.subjectRandom Forest (RF)
dc.subjectRize
dc.subjectSusceptibility
dc.subjectXGBoos
dc.titleA comparison of different machine learning models for landslide susceptibility mapping in Rize (Türkiye)
dc.typeArticle

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