공간 예측 모델을 이용한 산사태 재해의 인명 위험평가

Life Risk Assessment of Landslide Disaster Using Spatial Prediction Model

  • 투고 : 2006.07.24
  • 심사 : 2006.12.18
  • 발행 : 2006.12.30

초록

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

키워드

참고문헌

  1. 국립방재연구소, 2001, 홍수재해지도 작성 제도화 및 침수예상지역 추정 방법 개발(1)
  2. 김명국, 1997, 경상지역의 산사태 문제 분석에 대한 연구, 배재대학교 석사학위논문
  3. 류주형, 이사로, 원중선, 2002, 인공신경망을 이용한 산사태 발생요인의 가중치 결정, 한국자원환경지질학회, 35(1), 67-74
  4. 마호섭, 1994, 산지사면의 붕괴위험도 예측모델의 개발 및 실용화 방안, 한국임학회지, 83(2), 175-190
  5. 박노욱, 지광훈, Chung, F.C. , 권병두, 2003, 우도 비함수와 베이지안 결합을 이용한 공간통합 의 산사태 취약성 분석에의 적용, 한국지구과학학회지, 24(5), 428-439
  6. 박노욱, 지광훈, 장동호, 2004, 통계적 공간통합 모델을 이용한 태풍루사로 발생한 강릉지역 산사태 취약성 분석, 한국지형학회지, 11(4), 69-80
  7. 박노욱, 지광훈, Chung, F.C. , 권병두, 2005, 산사태 취약성 분석을 위한 GIS 기반 획률론적 추정모델과 모수적 모델의 적용, 자원환경지질학회지, 38(1), 45-55
  8. 보은군, 1999, 보은군수해백서
  9. 보은군, 2004, 보은군 통계연보
  10. 신영수, 1999, 산사태 발생 추정 요소에 관한 연구, 단국대학교 석사학위논문
  11. 신은선, 1996, 지리정보시스템 (GIS)을 이용한 보령.서천지역의 산사태 분석, 충남대학교 석사학위논문
  12. 이사로, 1999, GIS를 이용한 산사태 취약성 분석기법 개발 및 적용 연구, 연세대학교 박사학위 논문
  13. 이사로, 지광훈, 박노욱, 신진수, 2001, 산사태와 지형공간정보의 연관성 분석을 통한 장흥지역 산사태 취약성 분석, 자원환경지질, 34(2), 205-215
  14. 장동호, 박노욱, 지광훈, 김만규, Chung, F.C., 2004, GIS 기반 베이지안 예측모델을 이용한 보은지역의 산사태 취약성 분석, 한국지형학회지, 11(3), 13-23
  15. 최 경, 1986, 한국의 산사태 발생요인과 예지에 관한 연구, 강원대학교박사학위논문
  16. 한국지질자원연구원,2003, 산사태예측및 방지기술
  17. Bell, R. and Glade, T., 2004, Multi-hazard analysis in natural risk assessment. In Brebbia C. A., ed. , Risk Analysis IV, Southampton, Boston, WIT Press, 196-206
  18. Brand, E. W., 1985, Landslide in Hongkong Special Lecture, 8th Southeast Asian Geo. Conf., 1-15
  19. Chung, F. C. and Fabbri, A. G. , 2003 , Validation of spatial prediction models for landslide hazard mapping, Natural Hazards, 30, 451-472 https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
  20. Chung, F. C. and Fabbri , A. G., 1993, The representation of geoscience information for data integration, Nonrenewable Resources, 2, 122-139 https://doi.org/10.1007/BF02272809
  21. Chung, F. C. and Fabbri, A. G., 1998, Three Bayesian prediction models for landslide hazard , In, A. Bucciantti, ed. , Proc. of International Association for Mathernatical Geology Annual Meeting, 204-211
  22. Chung, F. C. and Fabbri, A. G., 1999, Probability prediction models for landslide hazard mapping, Photogrammetric Engineering & Remote Sensing, 65, 1389-1399
  23. Duda, R. O., Hart, P., and Nilsson, N., 1976, Subjective Bayesian methods for rulebased inference systems, Proceedings of the 1976 national Computer Conference, 1075-1082
  24. Einstein, H. H., 1988, Landslide risk assessment procedure, Proceeding of the fifth international symposium on landslide, 2, 1075-1090
  25. Fabbri, A. G., Chung, C. F., and Jang, D. H., 2004, A software approach to spatial predictions of natural hazards and consequent risks, In, Brebbia CA, ed., Risk Analysis IV. Southampton, Boston, WIT Press, 289-305
  26. Kornac, M., 2004, Statistical landslide prediction map as a basis for a risk map, In Brebbia C. A., ed. , Risk Analysis IV Southampton, Boston, WIT Press, 318-330
  27. Liguori, V. and Mortellaro, D. , 2004 Geomorphic hazard and risk in Platani's ba-sin: landslide risk valuation. In Brebbia C. A., ed. , Risk Analysis IV. South-ampton, Boston, WIT Press, 163-175
  28. Park, N. W., Chi, K. H., Chung, F. C., and Kwon, B. D. , 2003 , Application of spatial data integration based on the likelihood ratio function and bayesian rule for landslide hazard mapping, The Journal of Korean Earth Science Society, 24(5) , 428-439
  29. Varnes, D. J., 1984, Landslide hazard zonation: A review of principles and practice, United Nations Educational. Scientific and Cultural Organization, 9-59
  30. Wakens, A. T. and Koirala, N. P., 1986, Bulk appraisal of slope in Hong Kong, Landslide proc. 5th Int. Symp. on Landslide, A. A. Balkerna, 1181-1187