Landslide susceptibility analysis using GIS and artificial neural network

TLDR

The study develops and applies an artificial neural network–based landslide susceptibility analysis for Yongin, Korea. Landslide locations were mapped from aerial imagery, field surveys, and a spatial database, and key factors such as slope, curvature, soil properties, and timber characteristics were extracted and fed into a back‑propagation artificial neural network within a GIS to produce a susceptibility map that was validated against observed landslide sites. The ANN‑generated susceptibility map showed good agreement with observed landslides, demonstrating that GIS‑based analysis with neural networks can accurately predict hazards and aid land‑use planning. © 2003 John Wiley & Sons, Ltd.

Abstract

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. Copyright © 2003 John Wiley & Sons, Ltd.