Journal of the Korean GEO-environmental Society (한국지반환경공학회 논문집)

Korean Geo-Environmental Society (한국지반환경공학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Hazard Areas by Sediment Disaster Prediction Techniques Based on Ground Characteristics

지반특성을 고려한 토사재해 예측 기법별 위험지 분석

  • Received : 2017.10.25
  • Accepted : 2017.11.24
  • Published : 2017.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a predictive analysis was conducted on sediment disaster hazard area by selecting six research areas (Chuncheon, Seongnam, Sejong, Daejeon, Miryang and Busan) among the urban sediment disaster preliminary focus management area. The models that were used in the analysis were the existing models (SINMAP and TRIGRS) that are commonly used in predicting sediment disasters as well as the program developed through this study (LSMAP). A comparative analysis was carried out on the results as a means to review the applicability of the developed model. The parameters used in the predictions of sediment disaster hazard area were largely classified into topographic, soil, forest physiognomy and rainfall characteristics. A predictive analysis was carried out using each of the models, and it was found that the analysis using SINMAP, compared to LSMAP and TRIGRS, resulted in a prediction of a wider hazard zone. These results are considered to be due to the difference in analysis parameters applied to each model. In addition, a comparison between LSMAP, where the forest physiognomy characteristics were taken into account, and TRIGRS showed that similar tendencies were observed within a range of -0.04~2.72% for the predicted hazard area. This suggests that the forest physiognomy characteristics of mountain areas have diverse impacts on the stability of slopes, and serve as an important parameter in predicting sediment disaster hazard area.

본 연구에서는 도심지 토사재해 예비중점관리 대상지역 중 총 6개 연구지역(춘천, 성남, 세종, 대전, 미량, 부산)을 선정하여 토사재해 위험지 예측 분석을 실시하였다. 분석에 사용된 모델은 현재 토사재해 위험지 예측에 보편적으로 사용되고 있는 기존 모델(SINMAP, TRIGRS)과 본 연구를 통해 개발된 프로그램(LSMAP)을 활용하였으며, 결과 비교분석을 통해 개발모델의 적용성을 검토하였다. 토사재해 위험지 예측에 사용되는 매개변수는 크게 지형특성, 토질특성, 임상특성, 강우특성으로 분류하였으며, 각 모델에 따른 토사재해 위험지 예측 분석 결과 LSMAP 및 TRIGRS에 비해 SINMAP을 이용한 분석은 대체로 위험지를 광범위하게 예측하였다. 이러한 결과는 모델별 적용되는 분석 매개변수의 차이에 의한 것으로 판단된다. 또한 임상특성을 고려한 LSMAP은 TRIGRS 결과와 비교하였을 때 예측 위험지 기준 -0.04~2.72%의 범위 내로 유사한 경향을 보이는 것으로 분석되었다. 이는 산지에 분포하는 임상 정보가 비탈면 안정에 다양한 영향을 미치는 것이라 할 수 있으며, 토사재해 위험지 예측에 중요한 매개변수임을 알 수 있다.

Keywords

References

  1. Baum, R. L., Savage, W. Z. and Godt, J. W. (2002), TRIGRS-A fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, U.S. Geological Survey Open-File Report 02-424, p. 75.
  2. Baum, R. L., Mckenna, J. O., Godt, J. W., Harp, E. L. and McMullen, S. R. (2005), Hydrologic of landslide-prone coastal bluffs near Edmonds and Everett, Washington, 2001-2004, U.S. Geological Survey Open-File Report 2005-1063, p. 42.
  3. Baum, R. L., Godt, J. W. and Savage, W. Z. (2010), Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration, Journal of Geophysical Research, Vol. 115, Issue F03013, pp. 1-26.
  4. Choi, W. I., Kim, D. H. and Jeon, S. K. (2012), Forecasting technique of landslide risk zone by SINMAP analysis, An Academic Conference of Korean Society of Hazard Mitigation, 228 p. (in Korean).
  5. Choi, W. I. and Baek, S. C. (2017), Comparison of analysis model on soil disaster according to soil characteristics, Journal of the Korean Geo-Environmental Society, Vol. 18, No. 6, pp. 21-30 (in Korean).
  6. Choi, W. I., Choi, E. H. and Jeon, S. K. (2017), Comparison of sediment disaster risk depending on bedrock using LSMAP, Journal of Korean Geosynthetics Society, Vol. 16, No. 3, pp. 51-62 (in Korean).
  7. Hammond, C., Hall, D., Miller, S. and Swetik, P. (1992), Level i stability analysis (LISA) documentation for version 2.0, USDA Forest Service Intermountain Research Station, General Technical Report INT-285, p. 190
  8. Hong, W. P. (1991), Application of piles to landslides control, Korean Geotechnical Society, Vol. 7, No. 4, pp. 75-87 (in Korean).
  9. Hong, W. P., Kim, W. Y., Song, Y. S. and Lim, S. G. (2004), Prediction of landslide using artificial neural network model, Journal of the Korean Geotechnical Society, Vol. 20, No. 4, pp. 67-75 (in Korean).
  10. Iverson, R. M. (2000), Landslide triggering by rain infiltration, Water Resources Research, Vol. 36, No. 7, pp. 1897-1940. https://doi.org/10.1029/2000WR900090
  11. Jun, K. J. and Yune, C. Y. (2015), Analysis of slope hazard triggering factors through field investigation in Korea over The past four years, Journal of the Korean Geotechnical Society, Vol. 31, No. 5, pp. 47-58 (in Korean). https://doi.org/10.7843/KGS.2015.31.5.47
  12. Jung, K. H., Jung, S. J., Sonn, Y. K. and Hong, S. Y. (2007), Classification of hydrologic soil group of Korean soil, National Institute of Agricultural Science and Technology (in Korean).
  13. Kim, C. K., Bak, G. J., Kim, C. J., Song, Y. S. and Yun, J. M. (2013), Prediction of slope hazard probability around express way using decision tree model, Journal of the Korean Geosynthetincs Society, Vol. 12, No. 2, pp. 67-74 (in Korean).
  14. Kim, D. Y. (2013), Regional assessment of landslide susceptibility using a physically-based model, Unpublished Ph.D. dissertation, Dept. of Forest Sciences, Seoul National University (in Korean).
  15. Kim, G. N. (2001), Study on the method of the sediment disaster risk assessment of mountainous watershed using GIS, Unpublished Ph. D. Dissertation, Dept. of Forestry, Kangwon National University (in Korean).
  16. Kim, M. S., Moon, Y. H. and Lee, S. E. (2016), The improvement suggestion of a natural disaster management by debris-flow predict 3D simulation, The Magazine 2016 Korean Society of Hazard Mitigation, Vol. 16, No. 4, pp. 51-59 (in Korean).
  17. Kim, P. K. (2012), Numerical modeling for the detection and movement of debris flow using detailed soil maps and GIS, Unpublished Ph.D. dissertation, Dept. of Civil Engineering, Kyungpook National University (in Korean).
  18. Kim, W. Y., Chae, B. G., Kim, K. S., Cho, Y. C., Choi, Y. S., Lee, C. O., Lee, C. W., Kim, G. Y., Kim, J. H. and Kim, J. M. (2003), Prediction and mitigation of landslide hazards, Ministry of Science and Technology, KR-03-(T)-03, p. 339 (in Korean).
  19. Korea Forest Research Institute (2003), Tree resources evaluation program user's guide (in Korean).
  20. Korea Forest Service (2016), Forest Soil Digital Mapping, National Spatial Information Clearinghouse, GRS80/TM/1:25,000 (in Korean).
  21. Ma, H. S. and Jeong, W. O. (2007), Analysis of landslides characteristics in Korean national parks, Journal of Korean Forest Society, Vol. 96, No. 6, pp. 611-619 (in Korean).
  22. Montgomery, D. R. and Dietrich, W. E. (1994), A physically based model for the topographic control on shallow landsliding, Water Resources Research, Vol. 30, No. 4, pp. 1153-1171. https://doi.org/10.1029/93WR02979
  23. National Disaster Management Research Institute (2004), A study on the debris-flow and mitigation techniques, 11-1310148-000132-01 (in Korean).
  24. Oh, J. and Park, H. J. (2014), Analysis of landslide triggering rainfall threshold for prediction of landslide occurrence, Journal of Korean Society Hazard Mitigation, Vol. 14, No. 2, pp. 115-129 (in Korean). https://doi.org/10.9798/KOSHAM.2014.14.2.115
  25. Oh, S. R. and Lee, G. H. (2014), Slope stability analysis at catchment scale using spatially-distributed wetness index, Journal of Korean Geographers, Vol. 3, No. 2, pp. 111-126 (in Korean). https://doi.org/10.25202/JAKG.3.2.3
  26. Park, C. M., Ma, H. S., Kang, W. S., Oh, K. W., Park, S. H. and Lee, S. J. (2010), Analysis of landslide characteristics in Jeonlabuk-do, Korea, Journal of Agriculture & Life Science, Vol. 44, No. 4, pp. 9-20 (in Korean).
  27. Robert, T. P., Tarboton, D. G., Goodwin, C. N. and Prasad, A. (2006), SINMAP2 user's manual, pp.4-10.
  28. Roering, J. J., Schmidt, K. M., Stock, J. D., Dietrich, W. E. and Montgomery, D. R. (2003), Shallow landsliding, root reinforcement, and the spatial distribution of trees in the oregon coast range, Canadian Geotechnical Journal, Vol. 40, No. 2, pp. 237-253. https://doi.org/10.1139/t02-113
  29. Salciarini, D., Tamagnini, C., Conversini, P. and Rapinesi, S. (2012), Spatially distributed rainfall thresholds for the initiation of shallow landslides, Natural Hazards, Vol. 61, Issue 1, pp. 229-245. https://doi.org/10.1007/s11069-011-9739-2
  30. Song, Y. S., Cho, Y. C., Seo, Y. S. and Ahn, S. R. (2009), Development and its application of computer program for slope hazards prediction using decision tree model, Journal of the Korean Society of Civil Engineers, Vol. 29, No. 2C, pp. 59-69 (in Korean).
  31. Sorbino, G., Sica, C. and Cascini, L. (2010), Susceptibility analysis of shallow landslides source areas using physically based models, Natural Hazards, Vol. 53, Issue 2, pp. 313-332. https://doi.org/10.1007/s11069-009-9431-y
  32. Vieira, B. C., Fernandes, N. F. and Filho, O. A. (2010), Shallow landslide prediction in the Serra do Mar, Sao Paulo, Brazil, Nat. Hazards Earth Syst. Sci., Vol. 10, pp. 1829-1837. https://doi.org/10.5194/nhess-10-1829-2010
  33. Water Management Information System, http://www.wamis.go.kr/, (2017. 09. 20).
  34. Won, S. Y. (2010), GIS based sediment disaster prediction, Unpublished Master Dissertation, Dept. of civil engineering, Gangneung-Wonju National University (in Korean).
  35. Wu, T. H. (1976), Investigation of landslides on prince of wales island, alaska, Ohio State Univ., Dept. of Civil Eng., Geotech. Eng. Rpt. No. 5, p. 93
  36. Yoon, S. K., Jang, S. M., Rhee, J. Y. and Cho, J, P. (2017), Analysis of future extreme rainfall under climate change over the landslide risk zone in urban areas, J. Korean Soc. Hazard Mitig., Vol. 17, No. 5, pp. 355-367 (in Korean).