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dc.contributor.authorNguyen, Quang-Khanh
dc.contributor.authorTien Bui, Dieu
dc.contributor.authorHoang, Nhat-Duc
dc.contributor.authorTrinh, Phan Trong
dc.contributor.authorNguyen, Viet-Ha
dc.contributor.authorYilmaz, Isik
dc.date.accessioned2018-02-14T13:14:54Z
dc.date.available2018-02-14T13:14:54Z
dc.date.created2017-05-10T09:44:25Z
dc.date.issued2017
dc.identifier.citationSustainability. 2017, 9 (5), .nb_NO
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/11250/2484713
dc.description.abstractThis study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques, i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.nb_NO
dc.language.isoengnb_NO
dc.relation.urihttp://www.mdpi.com/2071-1050/9/5/813
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides Using GISnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder2017 by the authors.nb_NO
dc.source.pagenumber24nb_NO
dc.source.volume9nb_NO
dc.source.journalSustainabilitynb_NO
dc.source.issue5nb_NO
dc.identifier.doi10.3390/su9050813
dc.identifier.cristin1469255
cristin.unitcode222,57,1,0
cristin.unitnameInstitutt for økonomi og IT
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal