Spatial Landslide Hazard Prediction Using Rainfall Probability and a Logistic Regression Model

Saro Lee, Joong Sun Won, Seong Woo Jeon, Inhye Park, Moung Jin Lee

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


The aims of this study were to apply and validate a logistic regression model for landslide hazard, considering rainfall probability and using a geographic information system. The study focused on the Deokeokri and Karisanri areas of Inje, South Korea. We chose logistic regression for its mathematical rigor and its use of implementation in GIS software. Rainfall probability is analyzed for a quantitative prediction of rainfall changes in the study area. The rainfall probability was calculated using the Gumbel distribution. Then, the probabilities of landslides in the study area in target years (1, 3, 10, 50, and 100 years in the future) were calculated assuming that landslides are triggered by daily rainfall of 202 mm or 3-day cumulative rainfall of 449 mm. Landslide hazard maps were developed for the two study areas, and the logistic regression coefficients for one area were applied to the other area to validate the method. In Karisanri, all recorded landslides were used for validation. Validation results for the 202-mm daily precipitation threshold in Karisanri showed an average accuracy of 79.14 %, whereas those for the 449-mm 3-day cumulative precipitation threshold showed an average accuracy of 81.31 %. A combination of rainfall probability and logistic regression with a GIS is an effective method for analyzing the possibility of future landslides.

Original languageEnglish
Pages (from-to)565-589
Number of pages25
JournalMathematical Geosciences
Issue number5
Publication statusPublished - 2015 Jul 20


  • GIS
  • Landslide hazard map
  • Logistic regression
  • Probability rainfall
  • Validation

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • General Earth and Planetary Sciences


Dive into the research topics of 'Spatial Landslide Hazard Prediction Using Rainfall Probability and a Logistic Regression Model'. Together they form a unique fingerprint.

Cite this