Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping

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dc.authorid0000-0002-9830-8585en_US
dc.contributor.authorŞahin, Emrehan Kutlug
dc.date.accessioned2021-06-23T19:17:09Z
dc.date.available2021-06-23T19:17:09Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe aim of the study is to compare four recent gradient boosting algorithms named as Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) for modelling landslide susceptibility (LS). In the first step of the study, the geodatabase including landslide inventory map and landslide conditioning factors was constructed. In the second step, chi-square (CHI) statistic-based feature selection (FS) technique was utilized to compute the importance of the landslide causative factors. In the third step, tree-based ensemble learning algorithms were applied to predict the potential distribution of landslide susceptibility. Also, the prediction performance of ensemble methods was compared to that of Random Forest (RF) ensemble method. Finally, the prediction capabilities of the methods were assessed using overall accuracy (Acc), area under the receiver operating characteristic curve (AUC), kappa index, root mean square error (RMSE), and F score measures. In order to further evaluation, the McNemar's test was utilized to assess statistical significance in the differences between the four gradient boosting models. The accuracy results indicated that the CatBoost model had the highest prediction capability (Acc= 0.8503 and AUC= 0.8975), followed by the XGBoost (Acc= 0.8336 and AUC= 0.8860), the LightGBM (Acc= 0.8244 and AUC= 0.8796) and the GBM (Acc= 0.8080 and AUC= 0.8685). On the other hand, the estimated accuracy measures considered in this study showed that the RF method had the lowest prediction capability of compared the others. Although the individual performances of the methods were found to be acceptable level, the CatBoost method showed the superior performance compared to others with respect to the AUC and Acc values estimated in this study. The results of the study confirmed that the relatively new ensemble learning techniques were efficient and robust for producing LS maps and furthermore, it is probably that these algorithms will be preferred more often in the future studies due to their robustness.en_US
dc.identifier.doi10.1080/10106049.2020.1831623
dc.identifier.issn1010-6049
dc.identifier.scopus2-s2.0-85092690763en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1080/10106049.2020.1831623
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5250
dc.identifier.wosWOS:000577651100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞahin, Emrehan Kutlug
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofGeocarto Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLandslide Susceptibilityen_US
dc.subjectCatBoosten_US
dc.subjectXGBoosten_US
dc.subjectLightGBMen_US
dc.subjectEnsemble Tree Methodsen_US
dc.titleComparative analysis of gradient boosting algorithms for landslide susceptibility mappingen_US
dc.typeArticleen_US

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