Environmental Earth Sciences

, Volume 68, Issue 5, pp 1443–1464 | Cite as

Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

  • Soyoung Park
  • Chuluong Choi
  • Byungwoo Kim
  • Jinsoo KimEmail author
Original Article


Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).


Frequency ratio (FR) Analytic hierarchy process (AHP) Logistic regression (LR) Artificial neural network (ANN) Landslide susceptibility index (LSI) 



This work was researched by the supporting project to educate GIS experts. Thanks are also extended to two anonymous reviewers who suggested some improvements to the manuscript.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Soyoung Park
    • 1
  • Chuluong Choi
    • 1
  • Byungwoo Kim
    • 1
  • Jinsoo Kim
    • 2
    Email author
  1. 1.Department of Spatial Information EngineeringPukyong National UniversityBusanRepublic of Korea
  2. 2.ZEN21SeoulRepublic of Korea

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