Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Analysis of large-scale flood inundation area using optimal topographic factors

지형학적 인자를 이용한 광역 홍수범람 위험지역 분석

  • Lee, Kyoungsang (Department of Disaster Prevention and Environmental Engineering, Kyungpook National University) ;
  • Lee, Daeeop (Department of Disaster Prevention and Environmental Engineering, Kyungpook National University) ;
  • Jung, Sungho (Department of Disaster Prevention and Environmental Engineering, Kyungpook National University) ;
  • Lee, Giha (Department of Disaster Prevention and Environmental Engineering, Kyungpook National University)
  • 이경상 (경북대학교 과학기술대학 건설방재공학과) ;
  • 이대업 (경북대학교 과학기술대학 건설방재공학과) ;
  • 정성호 (경북대학교 과학기술대학 건설방재공학과) ;
  • 이기하 (경북대학교 과학기술대학 건설방재공학과)
  • Received : 2018.01.22
  • Accepted : 2018.03.06
  • Published : 2018.06.30
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Abstract

Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds.

최근 기후변화와 이상기후의 영향으로 인한 홍수재해의 시 공간적 패턴의 변화가 복잡해짐에 따라 홍수범람 예측은 점점 어려워지고 있다. 이러한 기상이변에 따른 홍수피해를 예방하고 대응하기 위한 비구조적 대책으로 홍수위험등급 및 범람범위 등의 정보를 포함하고 있는 홍수위험지도의 작성이 필요하다. 실제로 고정밀도 홍수위험지도를 작성하기 위해서는 1차적으로 지형, 지질, 토지피복, 기상 등의 자료를 기반으로 강우-유출-범람해석을 통해 침수면적 및 침수깊이 등 범람 정보를 획득해야 되며, 2차적으로 피해액 산정을 위해 사회 경제와 관련된 다양한 DB를 필요로 한다. 하지만 개발도상국에서는 이러한 자료가 부족하고, 일부지역에서는 자료자체를 획득할 수가 없어 홍수위험지도 제작이 불가능하거나 그 정확도가 매우 낮은 실정이다. 본 연구에서는 ASTER 또는 SRTM과 같은 범용 지형자료로부터 주요 지형학적 인자를 선정하고, 선형이진분류법(Liner binary classifiers)과 ROC분석(Receiver Operation Characteristics)을 사용하여 실제 홍수유역을 유사하게 모의하는 최적 지형학적 인자를 도출하고, 이를 기반으로 광역 홍수범람지도를 작성하는 방안을 제시한다. 본 연구에서 제시하는 방법론의 정확도 검증을 위해 북한(2007), 방글라데시(2007), 인도네시아(2010), 태국(2011), 미얀마(2015) 5개국의 대규모 홍수범람에 대해 적용하였다. 실제 홍수범람 영상정보에서 획득된 침수면적과의 공간적 비교 검토 결과, 최저(38%, 방글라데시), 최고(78%)으로 평균적으로 약 60%의 정확도를 나타내는 것으로 분석되었다. 본 연구에서 제시하는 지형학적 인자 기반의 홍수범람지도 작성방법은 미계측유역에 대해서도 DEM만을 사용하여 홍수위험 지역을 쉽게 구분할 수 있다는 장점을 가지고 있어 1 2차원 범람해석 모형의 적용이 어려운 대유역에 대해 홍수범람 우려지역에 대한 공간정보를 제공해줄 수 있을 것으로 판단된다.

Keywords

References

  1. Bischetti, G. B., and Chiaradia, E. A. (2010). "Calibration of distributed shallow landslides models in forested landscapes." Journal of Agricultural Engineering, Vol. 41, No. 3, pp. 23-35. https://doi.org/10.4081/jae.2010.3.23
  2. Centor, R. M. (1991) "Signal detectability: the use of ROC curves and their analyses." SAGE Journals, SAGE, June 1, pp. 102-106.
  3. Degiorgis, M., Gnecco, G., Gorni, S., Roth, G., Sanguineti, M., and Taramasso, A. C. (2012). "Classifiers for the detection of flood-prone areas using remote sensed elevation data." Journal of Hydrology, Vol. 470, pp. 302-315.
  4. Drummond, C., and Holte, R. C. (2006). "Cost curves: An improved method for visualizing classifier performance." Machine Learning, Vol. 65, No. 1, pp. 95-130. https://doi.org/10.1007/s10994-006-8199-5
  5. Engelmann, B., Hayden, E., and Tasche, D. (2003). "Testing rating accuracy," Credit Risk, Vol. 16, January, pp. 82-86.
  6. Hjerdt, K., McDonnell, J., and Rodhe, A. (2004). "A new topographic index to quantify downslope controls on local drainage." Water Resources Research, Vol. 40, No. 5, pp. 1-6.
  7. Horritt, M. S., and Bates, P. D. (2002). "Evaluation of 1D and 2D numerical models for predicting river flood inundation." Journal of Hydrology, Vol. 268, No. 1, pp. 87-99. https://doi.org/10.1016/S0022-1694(02)00121-X
  8. Huang, J., and Kao, S. (2006). "Optimal estimator for assessing landslide model performance." Hydrology and Earth System Sciences Discussions, Vol. 10, No. 6, pp. 957-965. https://doi.org/10.5194/hess-10-957-2006
  9. Hwang, T. H., Jang, D. W., Kim, H. S., and Seo, B. H. (2004). "Flood Inundation analysis based on HEC-GeoRAS & HEC-RAS." Proceedings of the Korea Water Resources Association Conference, KWRA, pp. 737-741.
  10. Jo, W. H., Han, G. Y., and An, G. H. (2010). "Flood risk map using FLUMEN model." Journal of Civil Engineering, KSCE, Vol. 30, No. 2-B, pp. 169-177.
  11. Kania, D. N. (2016). Large-scale flood inundation modelling with multiple satellite-derived datasets in upper citarum river water-shed, Indonesia. Master thesis, Chungnam National University, pp. 40-51
  12. Kirkby, M. (1975). "Hydrograph modelling strategies. Processes in physical and human geography." Heinemann, Oxford, pp. 69-90.
  13. Lee, D. E. (2013). Flood inundation analysis applicability evaluation of slope structure grid-based model. Master thesis, Chungnam National University, pp. 99-100.
  14. Lee, J. H., Choi, J. J., Lee, H. C., and Lee, E. T. (2003) "Mapping of floodplain boundaries using high spatial resolution DEM." Journal of Civil Engineering, KSCE, Vol. 23, No. 6, pp. 471-478.
  15. Lee, W. H., (2004). Development of flood analysis system of watershed using hydraulic and hydrologic models. Ph. D. dissertation, Konkuk University, pp. 132-135.
  16. Manfreda, S., Leo, M. D., and Sole, A. (2011). "Detection of flood-prone areas using digital elevation models." Journal of Hydrologic Engineering, Vol. 16, No. 10, pp. 781-790. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000367
  17. Manfreda, S., Samela, C., Gioia, A., Consoli, G. G., Iacobellis, V., Giuzio, L., and Sole, A. (2015). "Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models." Natural Hazards, Vol. 79, No. 2, pp. 735-754. https://doi.org/10.1007/s11069-015-1869-5
  18. Nardi, F., Vivoni, E. R., and Grimaldi, S. (2006). "Investigation a floodplain scaling relation using a hydrogeomorphic delineation method." Water Resources Research, Vol. 42, No. 9, pp. 1-15.
  19. Sobehart, J., and Keenan, S. (2001). "Measuring default accurately." Credit Risk Special Report, Risk, Vol. 14, No. 3, pp. 31-33.
  20. Swiss Re (2012). "Natural Catastrophes and Man-Made Disasters in 2011." Sigma Web of Science(R), No. 2, pp. 14-17.
  21. Tate, E. C., Olivera, F., and Maidment, D. (1999). Floodplain mapping using HEC-RAS and ArcView GIS. Ph. D. dissertation, The University of Texas at Austin, Texas Austin, pp. 9-22.
  22. World Resources Institute (2015). "World's 15 Countries with the Most People Exposed to River Floods." March 05.