Journal of Korean Society for Geospatial Information Science (대한공간정보학회지)

Korea Spatial Information Society (대한공간정보학회)
권호 표지
DOI QR코드

DOI QR Code

Landslide Susceptibility Mapping Using Ensemble FR and LR models at the Inje Area, Korea

FR과 LR 앙상블 모형을 이용한 산사태 취약성 지도 제작 및 검증

  • Kim, Jin Soo (Department of Spatial Information Engineering, Pukyong National University) ;
  • Park, So Young (Graduate School of Earth Environmental Hazard System, Pukyong National University)
  • 김진수 (부경대학교 공간정보시스템공학과) ;
  • 박소영 (부경대학교 지구환경재해시스템사업단)
  • Received : 2016.12.05
  • Accepted : 2016.12.29
  • Published : 2017.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

This research was aimed to analyze landslide susceptibility and compare the prediction accuracy using ensemble frequency ratio (FR) and logistic regression at the Inje area, Korea. The landslide locations were identified with the before and after aerial photographs of landslide occurrence that were randomly selected for training (70%) and validation (30%). The total twelve landslide-related factors were elevation, slope, aspect, distance to drainage, topographic wetness index, stream power index, soil texture, soil sickness, timber age, timber diameter, timber density, and timber type. The spatial relationship between landslide occurrence and landslide-related factors was analyzed using FR and ensemble model. The produced LSI maps were validated and compared using relative operating characteristics (ROC) curve. The prediction accuracy of produced ensemble LSI map was about 2% higher than FR LSI map. The LSI map produced in this research could be used to establish land use planning and mitigate the damages caused by disaster.

본 연구의 목적은 인제읍을 대상으로 빈도비와 로지스틱 회귀분석 모델을 통합한 앙상블 모델을 이용하여 산사태 취약성을 분석하고, 예측 정확도를 비교하는 것이다. 산사태 위치는 산사태 발생 전 후에 촬영된 항공사진을 이용하여 추출되었다. 추출된 총 422개의 산사태는 산사태 취약성 분석을 위해 훈련용 (70%)과 검증용 (30%) 자료로 랜덤하게 분류되었다. 산사태 관련인자는 고도, 경사도, 경사향, 배수로부터의 거리, 토양수분지수, 하천강도지수, 토질, 유효토심, 영급, 경급, 밀도, 임상 등 총 12개의 인자를 이용하였다. 산사태 및 산사태 관련인자는 공간데이터베이스로 구축된 뒤 빈도비와 앙상블 모델을 이용하여 산사태와 산사태 관련 인자 간 상관관계를 분석하였다. 그 결과를 바탕으로 각 모델별 산사태 취약성 지도를 작성하였고, relative operating characteristics(ROC) 곡선을 이용하여 예측 정확도를 검증 및 비교하였다. 분석 결과, 앙상블 모델에 의해 작성된 산사태 취약성 지도는 75.2%의 예측 정확도를 보였고, 이 결과는 빈도비 모델에 의해 작성된 산사태 취약성 지도와 비교하여 예측 정확도가 약 2% 향상된 것으로 나타났다. 본 연구에서 작성된 산사태 취약성 지도는 향후 효과적인 토지이용 계획을 수립하고, 재난재해로 인한 피해를 경감시키는데 활용 가능할 것으로 판단된다.

Keywords

References

  1. Aghdam, I. N., Varzandeh, M. H. M. and Pradhan, B., 2016, Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran), Environmental Earth Sciences, Vol. 75, No. 7, pp. 1-20. https://doi.org/10.1007/s12665-015-4873-x
  2. Beven, K. J. and Kirkby, M. J., 1979, A physically based, variable contributing area model of basin hydrology, Hydrological Sciences Bulletin, Vol. 24, pp. 43-69. https://doi.org/10.1080/02626667909491834
  3. Choi., J., Oh, H., Won, J. S. and Lee, S., 2010, Validation of an artificial neural network model for landslide susceptibility mapping, Environmental Earth Science, Vol. 60, pp. 473-483. https://doi.org/10.1007/s12665-009-0188-0
  4. Gomez, H. and Kavzoglu, T., 2005, Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Engineering Geology, Vol. 78, No. 1, pp. 11-27. https://doi.org/10.1016/j.enggeo.2004.10.004
  5. Hong, H., Pradhan, B., Jebur, M. N., Bui, D. T., Xu, C. and Akgun, A., 2016, Spatial prediction of landslide hazard at the luxi area (china) using support vector machines, Environmental Earth Sciences, Vol. 75, No. 1, pp. 1-14. https://doi.org/10.1007/s12665-015-4873-x
  6. Kang, S., Lee, S., Nikhil, N. V. and Park, J., 2015. Analysis of differences in geomorphological characteristics on initiation of landslides and debris flows, Journal of Korean Society of Hazard Mitigation, Vol. 15, No. 2, pp. 249-258. https://doi.org/10.9798/KOSHAM.2015.15.2.249
  7. 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, Vol. 20, No. 3, pp. 313-321.
  8. Lee, S., Lee, M. and Won, J., 2004, Study on landslide using GIS and remote sensing at the Kangneung area (II) - landslide susceptibility mapping and cross-validation using the probability technique, Journal of Korean Society of Economic and Environmental Geology, Vol. 37, No. 5, pp. 521-532.
  9. Lee., S. and Sambath, T., 2006, Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models, Environmental Geology, Vol. 50, pp. 847-855. https://doi.org/10.1007/s00254-006-0256-7
  10. Moore, I. D., Grayson, R. B. and Ladson, A. R., 1991, Digital terrain modelling: a review of hydrological, geomorphological, and biological application. In: Beven KJ, Moore ID (eds) Terrain analysis and distributed modeling in hydrology. Advances in hydrological processes. Wiley, Chichester, pp. 3-30.
  11. Oh, H., 2010, Landslide susceptibility analysis and validation using weight -of-evidence model, Journal of the Geological Society of Korea, Vol. 46, No. 2, pp. 157-170.
  12. Son, J. W., Kim, K. T., Lee, C. H. and Choi, C. U., 2009, Analysis of landslide in Inje region using aerial photograph and GIS. Journal of the Korean Society for Geospatial Information System, Vol. 17, No. 2, pp. 61-69.
  13. Yilmaz, I., 2010, Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine, Environmental Earth Sciences, Vol. 61, No. 4, pp. 821-836. https://doi.org/10.1007/s12665-009-0394-9
  14. Youssef, A. M., Pradhan, B., Jebur, M. N. and El-Harbi, H. M., 2015, Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia, Environmental Earth Sciences, Vol. 73, No. 7, pp. 3745-3761. https://doi.org/10.1007/s12665-014-3661-3
  15. Zhang, Z., Yang, F., Chen, H., Wu, Y., Li, T., Li, W. and Liu, P., 2016, GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models, Environmental Earth Sciences, Vol. 75, No. 11, pp. 1-12. https://doi.org/10.1007/s12665-015-4873-x

Cited by

  1. GIS 기반 공간예측모델 비교를 통한 인도네시아 자바지역 산사태 취약지도 제작 vol.37, pp.5, 2017, https://doi.org/10.12652/ksce.2017.37.5.0927
  2. 공간최적화 모델을 활용한 환경계획의 공간화 방안 vol.21, pp.2, 2017, https://doi.org/10.13087/kosert.2018.21.2.27