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Landslide susceptibility analysis using GIS and artificial neural network
Author(s) -
Lee Saro,
Ryu JooHyung,
Min Kyungduck,
Won JoongSun
Publication year - 2003
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.593
Subject(s) - landslide , geographic information system , artificial neural network , geology , cartography , soil texture , remote sensing , hydrology (agriculture) , geotechnical engineering , computer science , soil science , geography , artificial intelligence , soil water
The purpose of this study is to develop landslide susceptibility analysis techniques using an articial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identied in the study area from interpretation of aerial photographs, eld 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 articial neural network methods. The landslide susceptibility index was calculated by the back‐propagation method, which is a type of articial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were veried 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 efciently analyse the vast amount of data, and an articial 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. Copyright © 2003 John Wiley & Sons, Ltd.