Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 974638, 26 pages
Research Article

Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models

1Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003IMT, 1432 Aas, Norway
2Faculty of Surveying and Mapping, Hanoi University of Mining and Geology, Dong Ngac, Tu Liem, Hanoi, Vietnam
3Department of Civil Engineering, Spatial and Numerical Modelling Research Group, Faculty of Engineering, Universiti Putra Malaysia, Selangor, 43400 Serdang, Malaysia

Received 1 April 2012; Accepted 24 April 2012

Academic Editor: Wei-Chiang Hong

Copyright © 2012 Dieu Tien Bui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest prediction capability. The model derived using DT has the lowest prediction capability. Compared to the logistic regression model, the prediction capability of the SVM models is slightly better. The prediction capability of the DT and NB models is lower.