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Environmental Earth Sciences

, 78:116 | Cite as

Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models

  • Prima Riza Kadavi
  • Chang-Wook LeeEmail author
  • Saro LeeEmail author
Original Article
  • 18 Downloads

Abstract

The logistic regression (LR) and decision tree (DT) models are widely used for prediction analysis in a variety of applications. In the case of landslide susceptibility, prediction analysis is important to predict the areas which have high potential for landslide occurrence in the future. Therefore, the purpose of this study is to analyze and compare landslide susceptibility using LR and DT models by running three algorithms (CHAID, exhaustive CHAID, and QUEST). Landslide inventory maps (762 landslides) were compiled by reference to historical reports and aerial photographs. All landslides were randomly separated into two data sets: 50% were used to establish the models (training data sets) and the rest for validation (validation data sets). 20 factors were considered as conditioning factors related to landslide and divided into five categories (topography, hydrology, soil, geology, and forest). Associations between landslide occurrence and the conditioning factors were analyzed, and landslide-susceptibility maps were drawn using the LR and DT models. The maps were validated using the area under the curve (AUC) method. The DT model running the exhaustive CHAID algorithm (prediction accuracy 90.6%) was better than the DT CHAID (AUC = 90.2%), LR (AUC = 90.1%), and DT QUEST (84.3%) models. The DT model running the exhaustive CHAID algorithm is the best model in this study. Therefore, all models can be used to spatially predict landslide hazards.

Keywords

Landslide susceptibility Logistic regression Decision tree Area under the curve Korea 

Notes

Acknowledgements

This research was part of a Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science, ICT, and supported by two National Research Foundation of Korea (NRF)-grants from the Korean government (MSIP) (Nos. 2015M1A3A3A02013416 and 2017R1A2B4003258), and the Korea Meteorological Administration Research and Development Program (Grant No. KMIPA 2015-3071). Also, the study was supported by a 2017 Research Grant from Kangwon National University.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Science EducationKangwon National UniversityChuncheonSouth Korea
  2. 2.Geological Research DivisionKorea Institute of Geoscience and Mineral Resources (KIGAM)DaejeonSouth Korea
  3. 3.Korea University of Science and TechnologyDaejeonSouth Korea

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