The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Lee Saro, Jeon Seong Woo, Oh Kwan-Young, Lee Moung-Jin

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, 'slope' yielded the highest weight value (1.330), and 'aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

Original languageEnglish
Pages (from-to)117-132
Number of pages16
JournalOpen Geosciences
Issue number1
Publication statusPublished - 2016 Feb 1

Bibliographical note

Funding Information:
This research were supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Minister of Science, ICT and Future Planning of Korea and Korea Environment Institute funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2014R1A1A1002704). The English in this document has been checked by at least two professional editors, both native speakers of English. For a certicate, please see:

Publisher Copyright:
© 2016 L. Saro et al., published by De Gruyter Open.


  • GIS
  • Korea
  • Landslide
  • artificial neural network
  • logistic regression
  • susceptibility map

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


Dive into the research topics of 'The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea'. Together they form a unique fingerprint.

Cite this