Quantitative assessment of landslide susceptibility on a regional scale using geotechnical databases developed from GIS-based maps

D. W. Park, S. R. Lee, N. V. Nikhil, S. Yoon, G. H. Go

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A practical application of a simple and economical solution to landslide susceptibility zonation using a geographic information system (GIS) was performed in Woomyeon Mountain, Seoul, Korea. The regional, physically based stability model of TRIGRS was used as the landslide susceptibility analysis. The accuracy of the model results depends primarily on a detailed knowledge of the study site and on the quality of the input parameters. However, the input data for the model is difficult to obtain because it not only requires test-based results but also spatial data. An alternative application method for a physically based model in wide area using either GIS-based soil textures or geology maps is proposed for landslide susceptibility zonation. From a spatial database, the input data for the TRIGRS model including the material strength and hydraulic properties were extracted. The validation results exhibited satisfactory agreement between the calculated susceptibility zonation using different input layers and the existing landslide location on the landslide inventory. The use of these types of spatial maps linked with suggested geotechnical information enables reasonable estimation of the regions susceptible to landslides. Although the accuracy of the proposed model needs improvement, this approach is very useful for preliminary spatio-temporal assessments over large areas.

Original languageEnglish
Pages (from-to)25-38
Number of pages14
JournalDisaster Advances
Volume7
Issue number5
StatePublished - 1 Dec 2014

Keywords

  • Database
  • GIS
  • Geology
  • Landslide
  • Soil texture
  • TRIGRS

Fingerprint

Dive into the research topics of 'Quantitative assessment of landslide susceptibility on a regional scale using geotechnical databases developed from GIS-based maps'. Together they form a unique fingerprint.

Cite this