A Support Vector Machine for Landslide Susceptibility Mapping in GangwonProvince, Korea

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/474083.do
DOI
10.3390/su9010048
Title
A Support Vector Machine for Landslide Susceptibility Mapping in GangwonProvince, Korea
Description
Saro Lee was supported by the Basic Research Project of the KoreaInstitute of Geoscience and Mineral Resources (KIGAM) funded by theMinister of Science, ICT and Future Planning of Korea. Hyung-Sup Jungand Soo-Min Hong were supported by the "Development of Scene Analysis &Surface Algorithms" project, funded by ETRI, which is a subproject of"Development of Geostationary Meteorological Satellite Ground Segment(NMSC-2016-01)" program funded by NMSC (National MeteorologicalSatellite Center) of KMA(Korea Meteorological Administration).
abstract
In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.
provenance
Made available in Cube on 2018-09-28T10:36:45Z (GMT). No. of bitstreams: 0
language
English
author
Lee, Saro
Hong, Soo-Min
Jung, Hyung-Sup
orcid
Lee, Saro/0000-0003-0409-8263; Jung, Hyung-Sup/0000-0003-2335-8438
accessioned
2018-09-28T10:36:45Z
available
2018-09-28T10:36:45Z
issued
2017
citation
SUSTAINABILITY(9): 1
issn
2071-1050
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/474083.do
Funder
과학기술정보통신부
Funding Program
한국지질자원연구원연구운영비지원
Project ID
1711051618
Jurisdiction
Rep.of Korea
Project Name
디지털매핑에 의한 통합 지질정보 제공기술 개발
rights
openAccess
subject
landslide
GIS
SVM
validation
sensitivity analysis
type
article


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