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dc.contributor.authorNguyen, Phong Tung
dc.contributor.authorTuyen, Tran Thi
dc.contributor.authorShirzadi, Ataollah
dc.contributor.authorPham, Binh Thai
dc.contributor.authorShahabi, Himan
dc.contributor.authorOmidvar, Ebrahim
dc.contributor.authorAmini, Ata
dc.contributor.authorEntezami, Heresh
dc.contributor.authorPrakash, Indra
dc.contributor.authorPhong, Tran Van
dc.contributor.authorVu, Thao Ba
dc.contributor.authorThanh, Tran
dc.contributor.authorSaro, Lee
dc.contributor.authorTien Bui, Dieu
dc.identifier.citationApplied Sciences. 2019, 9 (14), .en_US
dc.descriptionLicensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseen_US
dc.description.abstractWe proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleDevelopment of a Novel Hybrid Intelligence Approach for Landslide Spatial Predictionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2019 by the authors.en_US
dc.source.journalApplied Sciencesen_US

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