Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping

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dc.authorid0000-0001-9670-3023en_US
dc.authorid0000-0002-9830-8585en_US
dc.contributor.authorŞahin, Emrehan Kutluğ
dc.contributor.authorÇölkesen, İsmail
dc.date.accessioned2021-06-23T19:17:12Z
dc.date.available2021-06-23T19:17:12Z
dc.date.issued2019
dc.departmentBAİBÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractLandslide susceptibility mapping (LSM) is a major area of interest within the field of disaster risk management that involves planning and decision-making activities. Therefore, preparation of dataset, construction of predictive model and analysis of results are considered to be important stages for effective and efficient disaster management in LSM. In recent years, a large number of studies has mainly focused on the effects of using machine learning (ML) algorithms as a predictive model in LSM. Decision tree-based ensemble learning algorithms known as decision forest is one of the popular ML techniques based on a combination of several decision tree algorithms to construct an optimal prediction model. In this study, prediction performances of recently proposed decision tree-based ensemble-based algorithms namely canonical correlation forest (CCF) and rotation forest (RotFor) are tested on LSM. In order to compare their performances, popular ensemble learning algorithms including random forest (RF), AdaBoost and bagging algorithms are also considered. For this purpose, first, twelve conditioning factors are determined in the study area, Karabuk province of Turkey. Second, individual importance of the factors on LSM process is evaluated using Fischer score analysis and selected factors are used as an input dataset for the construction of landslide susceptibility prediction models of CCF, RotFor, RF, AdaBoost and bagging algorithms. For the assessment of the performances, overall accuracy (OA), success rate curves and the area under the curve (AUC) analysis are utilized. Furthermore, chi-squared-based McNemar's test and well-known accuracy measures known as receiver operating characteristic (ROC) curves are employed to evaluate the pairwise comparison of the ensemble learning methods. Results show that CCF method outperforms the RotFor method by about 4%, and there is no statistically significant difference between CFF and other methods.en_US
dc.identifier.doi10.1080/10106049.2019.1641560
dc.identifier.endpage1275
dc.identifier.issn1010-6049
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85070985199en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1253
dc.identifier.urihttps://doi.org/10.1080/10106049.2019.1641560
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5287
dc.identifier.volume36
dc.identifier.wosWOS:000482305900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞahin, Emrehan Kutluğ
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.subjectFeature Selectionen_US
dc.subjectAdvanced Decision Treeen_US
dc.subjectCanonical Correlation Foresten_US
dc.subjectBagging Methodsen_US
dc.subjectAdaBoosten_US
dc.titlePerformance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mappingen_US
dc.typeArticleen_US

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