한국지구과학회지 (Journal of the Korean earth science society)

  • 제24권5호
  • /
  • Pages.428-439
  • /
  • 2003
  • /
  • 1225-6692(pISSN)
  • /
  • 2287-4518(eISSN)
한국지구과학회 (The Korean Earth Science Society)
권호 표지

우도비 함수와 베이지안 결합을 이용한 공간통합의 산사태 취약성 분석에의 적용

Application of Spatial Data Integration Based on the Likelihood Ratio Function nad Bayesian Rule for Landslide Hazard Mapping

  • 지광훈 (한국지질자원연구원 지질자원정보센터) ;
  • ;
  • 권병두 (서울대학교 지구과학교육과) ;
  • 박노욱 (한국지질자원연구원 지질자원정보센터)
  • Chi, Kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Chung, Chang-Jo F. (Geological Survey of Canada) ;
  • Kwon, Byung-Doo (Department of Earth Science Education, Seoul National University) ;
  • Park, No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
  • 발행 : 2003.08.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

여러 지질재해 중에서 산사태로부터 피해를 최소화하기 위해서는 미래의 산사태에 대해 취약한 지역의 추정이 필요하다. 산사태 위험성의 정량적 분석을 목적으로, 본 논문에서는 확률론적 공간통합 방법인 베이지안 기법의 적용가능성에 대해서 논의하고자 한다. 우선 산사태 발생과 관련이 있는 여러 공간자료의 확률론적 표현을 위해 우도비 함수를 사용하였으며, 베이지안 결합 규칙을 이용하여 최종적으로 통합된 검증을 수행하였다. 이러한 방법의 적용가능성을 검토하기 위하여 1998년 여름 산사태 공간 분포의 분할을 통한 검증을 수행하였다. 이러한 방법의 적용가능성을 검토하기 위하여 1998년 여름 산사태로 피해를 입은 경기도 장흥지역을 대상으로 사례연구를 수행하였다. 사례연구 수행 결과, 우도비에 기반한 베이지안 공간 통합 기법은 효율적으로 다양한 공간 자료를 통합할 수 있었으며, 검증결과는 해석과 의사결정 보조자료로 이용될 수 있을 것으로 기대된다.

Landslides, as a geological hazard, have caused extensive damage to property and sometimes result in loss of life. Thus, it is necessary to assess vulnerable areas for future possible landslides in order to mitigate the damage they cause. For this purpose, spatial data integration has been developed and applied to landslide hazard mapping. Among various models, this paper investigates and discusses the effectiveness of the Bayesian spatial data integration approach to landslide hazard mapping. In this study, several data sets related to landslide occurrences in Jangheung, Korea were constructed using GIS and then digitally represented using the likelihood ratio function. By computing the likelihood ratio, we obtained quantitative relationships between input data and landslide occurrences. The likelihood ratio functions were combined using the Bayesian combination rule. In order for predicted results to provide meaningful interpretations with respect to future landslides, we carried out validation based on the spatial partitioning of the landslide distribution. As a result, the Bayesian approach based on a likelihood ratio function can effectively integrate various spatial data for landslide hazard mapping, and it is expected that some suggestions in this study will be helpful to further applications including integration and interpretation stages in order to obtain a decision-support layer.

키워드

참고문헌

  1. Bonham-Carter, G.F., 1994, Geographic information systems for geoscientists: modeling with GIS. Pergamon, New York, 398 p
  2. Burton, A. and Bathurst, J.C., 1998, Physically based modeling of shallow landslide sediment yield at a catchment scale. Environmental Geology, 35, 89-99 https://doi.org/10.1007/s002540050296
  3. Chi, K.-H., Shin, J.-S., and Park, N.-W., 2001, Quantitative evaluation of GIS-based landslide prediction model using prediction rate curve. Korean Journal of Remote Sensing, 17, 199-210
  4. Chung, C.F. and Fabbri, A G., 1998, Three Bayesian prediction models for landslide hazard. Proceedings of International Association for Mathematical Geology Annual Meeting (IAMG'98), Ischia, Italy, 204-211
  5. Chung, C.F. and Fabbri, A.G., 1999, Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing, 65, 1389-1399
  6. Chung, C.F. and Fabbri, A.G., 2003, Validation of spatial prediction models for landslide hazard mapping. Natural Hazard (in press)
  7. Dai, F.C., Lee, C.F, Li, J., and Xu, Z.W., 2001, Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40, 381-391 https://doi.org/10.1007/s002540000163
  8. Duda, R.O., Hart, P., and Nilsson, N., 1976, Subjective Bayesian methods for rule-based inference systems. Proceedings of the 1976 National Computer Conference, 45, AFlPS, 1075-1082
  9. Guzzetti, E., Carrarra, A., Cardinali, M., and Reichenbach, P., 1999, Landslide hazard evaluation: a review of current techniques and their application on a multiscale study, central Italy. Geomorphology, 31, 181-216 https://doi.org/10.1016/S0169-555X(99)00078-1
  10. Kwon, B.-D. and Oh, S.-H., 2002, Bayesian inversion of gravity and resistivity data: detection of lava tunnel. Journal of the Korean Earth Science Society, 23, 15-29
  11. Lee, S. and Min, K., 2001, Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40, 1095-113 https://doi.org/10.1007/s002540100310
  12. Luzi, L. and Aorianna, P., 1996, Application of statistical and GIS techniques to slope instability zonation (I : 50,000 Fabriano geological map sheet). Soil Dynamics and Earthquake Engineering, 15, 83-94 https://doi.org/10.1016/0267-7261(95)00031-3
  13. Moon, W., 1993, On mathematical representation and integration of multiple spatial geoscience data sets. Canadian Journal of Remote Sensing, 19, 251-255
  14. Pearl, J., 1997, Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers, San Mateo, Calif., 552 p
  15. Sivia, D.S., 1996, Data Analysis: A Bayesian Tutorial. Oxford University Press, New York, 240 p
  16. Wald, L., 1999, Some terms of reference in data fusion. IEEE Transactions on Geoscience and Remote Sensing, 37, 1190 -1193 https://doi.org/10.1109/36.763269
  17. Zhou, C.H., Lee, C.F, Li, J., and Xu, Z.W., 2002, On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology, 43, 197-207 https://doi.org/10.1016/S0169-555X(01)00130-1