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dc.contributor.authorTruong, Xuan-Luan
dc.contributor.authorMitamura, Muneki
dc.contributor.authorKono, Yasuyki
dc.contributor.authorRaghavan, Venkatesh
dc.contributor.authorYonezawa, Go
dc.contributor.authorTruong, Xuan-Quang
dc.contributor.authorDo, Hang Thi
dc.contributor.authorTien Bui, Dieu
dc.contributor.authorLee, Saro
dc.date.accessioned2019-01-28T09:57:31Z
dc.date.available2019-01-28T09:57:31Z
dc.date.created2018-06-23T08:33:56Z
dc.date.issued2018
dc.identifier.citationApplied Sciences. 2018, 8 (7).nb_NO
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/11250/2582572
dc.descriptionThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.nb_NO
dc.description.abstractThe objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving performance of landslide susceptibility model. The LMTree is a relative new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e. slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. Compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better, therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modelling. This research could provide useful results for landslide modelling in landslide prone areas.nb_NO
dc.description.abstractEnhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Treenb_NO
dc.language.isoengnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleEnhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Treenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2018 by the authors. Licensee MDPI, Basel, Switzerland.nb_NO
dc.source.pagenumber22nb_NO
dc.source.volume8nb_NO
dc.source.journalApplied Sciencesnb_NO
dc.source.issue7nb_NO
dc.identifier.doi10.3390/app8071046
dc.identifier.cristin1593405
cristin.unitcode222,57,1,0
cristin.unitnameInstitutt for økonomi og IT
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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