Journal
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
Volume 78, Issue 3, Pages 1911-1925Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10064-017-1202-5
Keywords
Landslides; Machine learning; Naive Bayes Trees; Bagging; GIS; India
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Landslide susceptibility assessment was performed using the novel hybrid model Bagging-based Naive Bayes Trees (BAGNBT) at Mu Cang Chai district, located in northern Viet Nam. The model was validated using the Chi-square test, statistical indexes, and area under the receiver operating characteristic curve (AUC). In addition, other models, namely the Rotation Forest-based Naive Bayes Trees (RFNBT), single Naive Bayes Trees (NBT), and Support Vector Machines (SVM), were selected for the comparison. Results show that the novel hybrid model (AUC=0.834) outperformed the RFNBT (0.830), SVM (0.805), and NBT (0.800). This indicates that the BAGNBT is a promising and better alternative method for landslide susceptibility modeling and mapping.
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