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dc.contributor.authorPham, Binh Thai
dc.contributor.authorTien Bui, Dieu
dc.contributor.authorDholakia, M.B
dc.contributor.authorPrakash, Indra
dc.contributor.authorPham, Viet-Ha
dc.contributor.authorMehmood, Khalid
dc.contributor.authorLe, Quoc-Hung
dc.date.accessioned2018-02-23T10:30:38Z
dc.date.available2018-02-23T10:30:38Z
dc.date.created2016-10-28T11:32:38Z
dc.date.issued2016
dc.identifier.citationGeomatics, Natural Hazards and Risk. 2016, 8:2, 649-671nb_NO
dc.identifier.issn1947-5705
dc.identifier.urihttp://hdl.handle.net/11250/2486698
dc.description.abstractThe objective of this study is to attempt a new soft computing approach for assessment of landslide susceptibility in the Luc Yen district, Yen Bai province (Viet Nam) using a novel classifier ensemble model of Naïve Bayes and Rotation Forest. First, history of 95 landslide locations was identified byfield investigations and interpretation of aerial photos. Also, the total ten landslide causal factors were selected (slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to rivers, distance to faults, and rainfall) to evaluate the spatial relationship with landslide occurrences. Information Gain technique is carried out to quantify the predictive capability of these factors. Second, landslide susceptibility assessment was carried out utilizing the novel classifier ensemble model. Finally, the performance of landslide model was validated using receiver operating characteristic curve technique, and statistical index-based evaluations. The novel classifier ensemble model indicates high prediction capability (AUC = 0.846) and relatively high accuracy (ACC = 78.77%). The study reveals that this model performs well in comparison to the other landslide models such as AdaBoost, Bagging, MultiBoost, and Random Forest. Overall, the novel classifier ensemble model is a promising method that could be used for landslide susceptibility assessment.nb_NO
dc.language.isoengnb_NO
dc.relation.urihttp://www.tandfonline.com/doi/full/10.1080/19475705.2016.1255667
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Novel Ensemble Classifier of Rotation Forest and Naïve Bayer for Landslide Susceptibility Assessment at the Luc Yen District, Yen Bai Province (Viet Nam) Using GISnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holderThe Authorsnb_NO
dc.source.pagenumber649-671nb_NO
dc.source.volume8nb_NO
dc.source.journalGeomatics, Natural Hazards and Risknb_NO
dc.source.issue2nb_NO
dc.identifier.doi10.1080/19475705.2016.1255667
dc.identifier.cristin1395347
cristin.unitcode222,57,1,0
cristin.unitnameInstitutt for økonomi og IT
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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