All Issue

2021 Vol.58, Issue 2

Research Paper

Comparison of Landslide Susceptibility Assessments Using Logistic Regression With a Study Site Extent Scale

연구지역 규모에 따른 로지스틱 회귀를 이용한 산사태 위험도 평가 결과 비교

Aran Yang1
,
Hyeong-Dong Park23*
양 아란1
,
박 형동23*
1Assistant Manager, Korea Spatial Information & Community Co. Ltd.
2Professor, Department of Energy Systems Engineering, Seoul National University
3Adjunct Researcher, Research Institute of Energy and Resources, Seoul National University
1한국공간정보통신 주임
2서울대학교 에너지시스템공학부 교수
3서울대학교 에너지자원신기술연구소 겸임연구원
*Corresponding Author
30 April 2021. pp. 87-99
PDF XML Citation
Abstract

To verify the effect of the spatial extent scale of a study area on the assessment of landslide susceptibility, ​​three spatial extents were set for the Gyeongnam province. The landslide susceptibility results for each scale were compared using logistic regression. Twelve influencing factors and a landslide inventory map constructed from landslide occurrence information from 2010 to 2018, collected through media such as news, reports, satellite images, and aerial images, were used. The results verified that discontinuous factors with different distributions, such as lithology and soil type, have a greater influence on the difference in the results according to the scale of change. The area under the curve (AUC) value decreased as the spatial scale increased due to a variance increase in the data distribution.

Keywords
Landslide susceptibility
Logistic regression
Spatial extent
GIS

본 연구는 연구지역의 공간적 규모가 산사태 위험도 평가 결과에 미치는 영향을 확인하기 위해 경남 지역에 세 가지 규모의 연구지역을 설정하고 각 규모별로 로지스틱 회귀를 사용한 산사태 위험도 평가를 수행하여 그 결과를 비교하였다. 본 연구에서는 산사태 위험도 평가에 빈번히 사용되는 12개 인자를 사용하였으며 뉴스, 보고서, 위성영상, 항공 영상 등 매체를 통해 수집한 2010년부터 2018년까지의 산사태 발생 정보를 사용하여 산사태 인벤토리 맵을 구축하였다. 로지스틱 회귀를 통한 분석 결과, 암종, 토양 종류와 같이 규모에 따라 분포가 상이하며 연속성이 없는 인자가 산사태 위험도 평가 결과에 영향을 미칠 수 있다는 점과 규모가 커짐에 따라 자료 분포의 분산 증가로 AUC(Area Under Curve) 값이 작아짐을 확인하였다.

키워드
산사태 위험도
로지스틱 회귀
공간 규모
지리정보시스템
References
1
Akgun, A., 2012. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir. Turkey Landslides, 9(1), p.93-106. 10.1007/s10346-011-0283-7
2
Chang, K.T., Merghadi, A., Yunus, A.P., Pham, B.T., and Dou, J., 2019. Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Scientific Reports, 9(1), 12296. 10.1038/s41598-019-48773-231444375PMC6707277
3
Choi, Y., Park, H.D., Sunwoo, C., and Jung, Y.B., 2009. Application of Fuzzy Theory and AHP to Evaluate the Slope Instability at Pasir Open Pit Coal Mine, Indonesia. Journal of the Korean Society of Mineral and Energy Resources Engineering, 46(1), p.45-60.
4
Dai, F.C., Lee, C.F., and Ngai, Y.Y., 2002. Landslide risk assessment and management: an overview. Engineering Geology, 64, p.65-87. 10.1016/S0013-7952(01)00093-X
5
Dragićević, S., Lai, T., and Balram, S., 2015. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International, 45, p.114-125. 10.1016/j.habitatint.2014.06.031
6
Haque, U., da Silva, P.F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., Wilopo, W., Andersen, P., Lu, P., Lee, J., Yamamoto, T., Keellings, D., Wu, J.H., and Glass, G.E., 2019. The human cost of global warming: Deadly landslides and their triggers (1995-2014). Science of the Total Environment, 682, p.673-684. 10.1016/j.scitotenv.2019.03.41531129549
7
Kim, W.Y., Lee, S., Kim, K.S., and Chae, B.G., 1998. Landslide types and susceptibilities related to geomorphic characteristics - Yeonchon-Chulwon area -. The Journal of Engineering Geology, 8(2), p.115-130.
8
Kim, Y.J., Kim, W.Y., and Yu, I.H., 1992. GIS technology for analyzing regional geologic hazards (Landslide). The Journal of Engineering Geology, 2(2), p.131-140.
9
Koo, Y.H., Kim, S.M., Oh, M.C., and Park, H.D., 2018. Landslide Risk Assessment at the Gumdeok Mine in North Korea using Satellite Images and GIS Spatial Data. Journal of the Korean Society of Mineral and Energy Resources Engineering, 55(4), p.259-271. 10.32390/ksmer.2018.55.4.259
10
Landslide Information System, 2021.01.28, https://sansatai.forest.go.kr/
11
Lee, C.W., Woo, C.S., Kim, D.Y., Chung, S.H., and Koo, K.S., 2014. Understanding Landslides for Public Safety and National Territorial Preservation, National Institute of Forest Science, Seoul, Korea, 71p.
12
Lee, H. and Kim, G., 2012. Landslide risk assessment in Inje using logistic regression model. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 30(3), p.313-321. 10.7848/ksgpc.2012.30.3.313
13
Lee, J.D., Yeon, S.H., Kim, S.G., and Lee, H.C., 2002. The application of GIS for the prediction of landslide-potential areas. Journal of the Korean Association of Geographic Information Studies, 5(1), p.38-47.
14
Lee, S. and Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), p.33-41. 10.1007/s10346-006-0047-y
15
Lee, S., 2019. Current and future status of GIS-based landslide susceptibility mapping: a literature review. Korean Journal of Remote Sensing, 35(1), p.179-193.
16
Lee, Y. J., Park, G. A., and Kim, S. J., 2006. Analysis of landslide hazard area using logistic regression analysis and AHP (Analytical Hierarchy Process) approach. Journal of The Korean Society of Civil Engineers D, 26(5D), p.861- 867.
17
Ministry of Economy and Finance, 2020. Participatory Budget Discussion Report for Forest Disaster Prevention and Recovery, Sejong, Korea, 11p.
18
National Territorial Information Platform, 2021.01.28, http://map.ngii.go.kr/
19
Oh, H.J., 2010. Landslide detection and landslide susceptibility mapping using aerial photos and artificial neural networks. Korean journal of remote sensing, 26(1), p.47-57.
20
Park, S.J., Lee, C.W., Lee, S., and Lee, M.J., 2018. Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea. Remote Sensing, 10(10), 1545. 10.3390/rs10101545
21
Pradhan, B. and Lee, S., 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), p.747-759. 10.1016/j.envsoft.2009.10.016
22
Shin, H.W. and Lee, S.G., 2018. Comparison of landslide susceptibility analysis considering the characteristics of landslide trigger points. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 36(2), p.59-66.
23
Süzen, M.L. and Doyuran, V., 2004. A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology, 45(5), p.665-679. 10.1007/s00254-003-0917-8
24
Xu, Z., Kwak, H., Lee, W.K., Park, T., Kwon, T.H., and Park, S., 2011. Assessment of landslide on climate change using GIS. Journal of Climate Change Research, 2(1), p.43-54.
25
Yilmaz, I., 2009. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4), p.821-836. 10.1007/s12665-009-0394-9
Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 58
  • No :2
  • Pages :87-99
  • Received Date : 2021-02-09
  • Revised Date : 2021-03-22
  • Accepted Date : 2021-04-27
  • DOI :https://doi.org/10.32390/ksmer.2021.58.2.087
Journal Informaiton Journal of the Korean Society of Mineral and Energy Resources Engineers Journal of the Korean Society of Mineral and Energy Resources Engineers
Online Submission
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close