Landslide susceptibility analysis using GIS and artificial neural network

Saro Lee, Joo Hyung Ryu, Kyungduck Min, Joong Sun Won

Research output: Contribution to journalArticle

159 Citations (Scopus)

Abstract

The purpose of this study is to develop landslide susceptibility analysis techniques using an artificial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs, field survey data, and a spatial database of the topography, soil type and timber cover. The landslide-related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by artificial neural network methods. The landslide susceptibility index was calculated by the back-propagation method, which is a type of artificial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were verified using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to efficiently analyse the vast amount of data, and an artificial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction.

Original languageEnglish
Pages (from-to)1361-1376
Number of pages16
JournalEarth Surface Processes and Landforms
Volume28
Issue number12
DOIs
Publication statusPublished - 2003 Nov

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Earth-Surface Processes
  • Earth and Planetary Sciences (miscellaneous)

Fingerprint Dive into the research topics of 'Landslide susceptibility analysis using GIS and artificial neural network'. Together they form a unique fingerprint.

  • Cite this