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dc.contributor.authorQuang-Khanh Nguyen
dc.contributor.authorDieu Tien Bui
dc.contributor.authorNhat-Duc Hoang
dc.contributor.authorPhan Trong Trinh
dc.contributor.authorViet-Ha Nguyen
dc.contributor.authorYilmaz, Isik
dc.date.accessioned2019-07-27T12:10:23Z
dc.date.accessioned2019-07-28T09:41:16Z
dc.date.available2019-07-27T12:10:23Z
dc.date.available2019-07-28T09:41:16Z
dc.date.issued2017
dc.identifier.issn2071-1050
dc.identifier.urihttps://dx.doi.org/10.3390/su9050813
dc.identifier.urihttps://hdl.handle.net/20.500.12418/6784
dc.descriptionWOS: 000404127800138en_US
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.en_US
dc.description.sponsorshipDepartment of Business and IT, School of Business, University College of Southeast Norway, Bo i Telemak, Norway; Geographic Information System groupen_US
dc.description.sponsorshipThis research was supported by the Geographic Information System group, Department of Business and IT, School of Business, University College of Southeast Norway, Bo i Telemak, Norway. The authors would like to thank four anonymous reviewers for their valuable and constructive comments on the earlier version of the manuscript.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.isversionof10.3390/su9050813en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectlandslideen_US
dc.subjectclassifier ensembleen_US
dc.subjectinstance based learningen_US
dc.subjectRotation Foresten_US
dc.subjectGISen_US
dc.subjectVietnamen_US
dc.titleA Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides Using GISen_US
dc.typearticleen_US
dc.relation.journalSUSTAINABILITYen_US
dc.contributor.department[Quang-Khanh Nguyen] Hanoi Univ Min & Geol, Fac Informat Technol, Hanoi 100000, Vietnam -- [Dieu Tien Bui] Univ Coll Southeast Norway, Dept Business & IT, Geog Informat Syst Grp, Gullbringvegen 36, N-3800 Bo I Telemark, Norway -- [Nhat-Duc Hoang] Duy Tan Univ, Inst Res & Dev, Fac Civil Engn, P809-K7-25 Quang Trung, Danang 556361, Vietnam -- [Phan Trong Trinh] Vietnam Acad Sci & Technol VASC, Inst Geol Sci, 84 Chua Lang St, Hanoi 100000, Vietnam -- [Viet-Ha Nguyen] Hanoi Univ Min & Geol, Fac Geomat & Land Adm, Hanoi 100000, Vietnam -- [Yilmaz, Isik] Cumhuriyet Univ, Dept Geol Engn, Fac Engn, TR-58140 Sivas, Turkeyen_US
dc.contributor.authorIDBui, Dieu Tien -- 0000-0001-5161-6479; YILMAZ, ISIK -- 0000-0001-8750-8736en_US
dc.identifier.volume9en_US
dc.identifier.issue5en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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