Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey

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dc.authorid0000-0003-2232-2011en_US
dc.authorid0000-0002-9957-1692en_US
dc.authorid0000-0002-4535-9143en_US
dc.contributor.authorUsta, Ziya
dc.contributor.authorAkıncı, Halil
dc.contributor.authorAkın, Alper Tunga
dc.date.accessioned2024-11-26T10:31:18Z
dc.date.available2024-11-26T10:31:18Z
dc.date.issued2024
dc.departmentAÇÜ, Mühendislik Fakültesi, Harita Mühendisliği Bölümüen_US
dc.description.abstractTurkey’s Artvin province is prone to landslides due to its geological structure, rugged topography, and climatic characteristics with intense rainfall. In this study, landslide susceptibility maps (LSMs) of Murgul district in Artvin province were produced. The study employed tree-based ensemble learning algorithms, namely Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and eXtreme Gradient Boosting (XGBoost). LSM was performed using 13 factors, including altitude, aspect, distance to drainage, distance to faults, distance to roads, land cover, lithology, plan curvature, profile curvature, slope, slope length, topographic position index (TPI), and topographic wetness index (TWI). The study utilized a landslide inventory consisting of 54 landslide polygons. Landslide inventory dataset contained 92,446 pixels with a spatial resolution of 10 m. Consistent with the literature, the majority of landslide pixels (70% – 64,712 pixels) were used for model training, and the remaining portion (30% – 27,734 pixels) was used for model validation. Overall accuracy, precision, recall, F1-score, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC-ROC) were considered as validation metrics. LightGBM and XGBoost were found to have better performance in all validation metrics compared to other algorithms. Additionally, SHapley Additive exPlanations (SHAP) were utilized to explain and interpret the model outputs. As per the LightGBM algorithm, the most influential factors in the occurrence of landslide in the study area were determined to be altitude, lithology, distance to faults, and aspect, whereas TWI, plan and profile curvature were identified as the least influential factors. Finally, it was concluded that the produced LSMs would provide significant contributions to decision makers in reducing the damages caused by landslides in the study area.
dc.identifier.doi10.1007/s12145-024-01259-w
dc.identifier.endpage1481en_US
dc.identifier.issn1865-0473
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage1459en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s12145-024-01259-w
dc.identifier.urihttps://hdl.handle.net/11494/5024
dc.identifier.volume17en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnsemble Learning Algorithmsen_US
dc.subjectLandslideen_US
dc.subjectLandslide Susceptibilityen_US
dc.subjectMachine Learningen_US
dc.subjectMurgulen_US
dc.titleComparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkeyen_US
dc.typeArticle

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