Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea

Jeong Cheol Kim, Sunmin Lee, Hyung Sup Jung, Saro Lee

Research output: Contribution to journalArticlepeer-review

196 Scopus citations

Abstract

Landslides susceptibility maps were constructed in the Pyeong-Chang area, Korea, using the Random Forest and Boosted Tree models. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models. Seventeen landslide-related factors were extracted and constructed in a spatial database. The relationships between the observed landslide locations and these factors were identified by using the two models. The models were used to generate a landslide susceptibility map and the importance of the factors was calculated. Finally, the landslide susceptibility maps were validated. Finally, landslide susceptibility maps were generated. For the Random Forest model, the validation accuracy in regression and classification algorithms showed 79.34 and 79.18%, respectively, and for the Boosted Tree model, these were 84.87 and 85.98%, respectively. The two models showed satisfactory accuracies, and the Boosted Tree model showed better results than the Random Forest model.

Original languageEnglish
Pages (from-to)1000-1015
Number of pages16
JournalGeocarto International
Volume33
Issue number9
DOIs
StatePublished - 2 Sep 2018

Keywords

  • GIS, Korea
  • Landslide susceptibility
  • boosted tree
  • random forest

Fingerprint

Dive into the research topics of 'Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea'. Together they form a unique fingerprint.

Cite this