skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

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

Mathematical Problems in Engineering, 2012-01, Vol.2012, p.1-26 [Peer Reviewed Journal]

Copyright © 2012 Dieu Tien Bui et al. ;Copyright © 2012 Dieu Tien Bui et al. 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. ;ISSN: 1024-123X ;EISSN: 1563-5147 ;DOI: 10.1155/2012/974638

Full text available

Citations Cited by
  • Title:
    Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
  • Author: Tien Bui, Dieu ; Pradhan, Biswajeet ; Lofman, Owe ; Revhaug, Inge
  • Hong, Wei-Chiang
  • Subjects: Accuracy ; Bayesian analysis ; Data mining ; Decision trees ; Emergency preparedness ; Inventories ; Landslides ; Landslides & mudslides ; Life sciences ; Logistics ; Mathematical models ; Methods ; Probability ; Stockpiling ; Studies ; Support vector machines ; Training
  • Is Part Of: Mathematical Problems in Engineering, 2012-01, Vol.2012, p.1-26
  • Description: 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.
  • Publisher: New York: Hindawi Limiteds
  • Language: English
  • Identifier: ISSN: 1024-123X
    EISSN: 1563-5147
    DOI: 10.1155/2012/974638
  • Source: ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait