Ecology and Resilient Infrastructure

Korean Society of Ecology and Infrastructure Engineering (응용생태공학회)
권호 표지
DOI QR코드

DOI QR Code

ROC Analysis of Topographic Factors in Flood Vulnerable Area considering Surface Runoff Characteristics

지표 유출 특성을 고려한 홍수취약지역 지형학적 인자의 ROC 분석

  • Lee, Jae Yeong (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Ji-Sung (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
  • 이재영 (한국건설기술연구원 국토보전연구본부) ;
  • 김지성 (한국건설기술연구원 국토보전연구본부)
  • Received : 2020.11.24
  • Accepted : 2020.12.04
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The method of selecting an existing flood hazard area via a numerical model requires considerable time and effort. In this regard, this study proposes a method for selecting flood vulnerable areas through topographic analysis based on a surface runoff mechanism to reduce the time and effort required. Flood vulnerable areas based on runoff mechanisms refer to those areas that are advantageous in terms of the flow accumulation characteristics of rainfall-runoff water at the surface, and they generally include lowlands, mild slopes, and rivers. For the analysis, a digital topographic map of the target area (Seoul) was employed. In addition, in the topographic analysis, eight topographic factors were considered, namely, the elevation, slope, profile and plan curvature, topographic wetness index (TWI), stream power index, and the distances from rivers and manholes. Moreover, receiver operating characteristic analysis was conducted between the topographic factors and actual inundation trace data. The results revealed that four topographic factors, namely, elevation, slope, TWI, and distance from manholes, explained the flooded area well. Thus, when a flood vulnerable area is selected, the prioritization method for various factors as proposed in this study can simplify the topographical analytical factors that contribute to flooding.

본 연구에서는 홍수해석 등 수치모형을 이용한 기존의 홍수위험지역 선정 시 필요한 시간과 노력을 절감하고자 유출메커니즘 기반의 지형 분석을 통해 홍수취약지역을 제시하고자 한다. 유출메커니즘 기반의 홍수취약지역은 강우-유출수의 지표면 흐름누적 특성에 유리한 지역으로 일반적으로 저지대, 완경사, 하천 등이 해당된다. 분석을 위해 대상지역인 서울시의 수치지형도를 이용하여 표고, 경사도, 수직 및 수평 사면 곡률, 지표습윤계수 (Topographic Wetness Index, TWI), 유수력 지수 (Stream Power Index, SPI), 하천 및 맨홀과의 거리 등 8개의 지형학적 인자를 고려하였다. 지형학적 인자들과 실제 침수흔적자료와의 ROC (Receiver Operation Characteristic) 분석 결과, 표고, 경사도, 지표습윤계수, 맨홀과의 거리 등 4개의 지형학적 인자가 침수지역을 잘 설명하는 것으로 나타났다. 홍수취약지역 선정 시 본 연구에서 제안하는 다양한 인자에 대한 우선순위 산정 방안은 홍수에 기여하는 지형학적 분석 요소를 간소화 시킬 수 있을 것으로 판단된다.

Keywords

References

  1. Arrighi, C., Alcerreca-Huerta, J.C., Oumeraci, H., and Castelli, F. 2015. Drag and lift contribution to the incipient motion of partly submerged flooded vehicles. Journal of Fluids and Structures 57: 170-184. https://doi.org/10.1016/j.jfluidstructs.2015.06.010
  2. Arrighi, C., Oumeraci, H., and Castelli, F. 2017. Hydrodynamics of pedestrians' instability in floodwaters. Hydrology and Earth System Sciences 21: 515-531. https://doi.org/10.5194/hess-21-515-2017
  3. Bae, H.B. and Kwon, O.S. 2020. Untact face recognition system based on super-resolution in low-resolution images. Journal of Korea Multimedia Society 23: 412-420. (in Korean)
  4. Beven, K.J. and Kirkby, M.J. 1979. A physically based variable contributing area model of basin hydrology. Hydrological Sciences Journal 24: 43-69. https://doi.org/10.1080/02626667909491834
  5. Chang, L.C., Shen, H.Y., and Chang, F.J. 2014. Regional flood inundation nowcast using hybrid SOM and dynamic neural networks. Journal of Hydrology 519: 476-489. https://doi.org/10.1016/j.jhydrol.2014.07.036
  6. Cho, W.H., Han, K.Y., and Ahn, K.H. 2010. Flood risk mapping with FLUMEN model application. Journal of Korea Society of Civil Engineering 30: 169-177. (in Korean)
  7. Chung, C.H., Chiang, Y.M., and Chang, F.J. 2012. A spatial neural fuzzy network for estimating pan evaporation at ungauged sites. Hydrology and Earth System Sciences 255: 255-266.
  8. DeLong, E.R., DeLong, D.M., and Clarke-Pearson, D.L. 1988. Comparing the areas under two or more correlated receiver operating characteristic. Biometrics 44: 837-845. https://doi.org/10.2307/2531595
  9. Ji, M.H. and Cho, H.J. 2017. Analysis of changes of flood inundation depth and area according to channel migration and river improvement using HEC-GeoRAS. Journal of Korea Water Resources Association 50: 315-324. (in Korean) https://doi.org/10.3741/JKWRA.2017.50.5.315
  10. Jung, M.K., Kim, J.G., Uranchimeg, S., and Kwon, H.H. 2020. The probabilistic estimation of inundation region using a multiple logistic regression. Journal of Korea Water Resources Association 53: 121-129. (in Korean) https://doi.org/10.3741/JKWRA.2020.53.2.121
  11. Kim, H.I., Keum, H.J., and Han, K.Y. 2019. Real-time urban inundation prediction combining hydraulic and probabilistic methods. Water 11: 293-311. https://doi.org/10.3390/w11020293
  12. Kim, T.H., Han, K.Y., and Park, J.H. 2016. New flood hazard mapping using runoff mechanism on Gamcheon watershed. Journal of Korea Society of Civil Engineering 36: 1011-1021. (in Korean) https://doi.org/10.12652/Ksce.2016.36.6.1011
  13. Lagadec, L.R., Patrice, P., Braud, I., Charzelle, B., Moulin, L., Dehotin, J., and Breil, P. 2016. Description and evaluation of a surface runoff susceptibility mapping method. Journal of Hydrology 541: 405-509.
  14. Lee, J.Y., Han, K.Y., and Kim, H.I. 2019. Mapping technique for flood vulnerable area using surface runoff mechanism. Journal of the Korean Association of Geographic Information Studies 22: 181-196. (in Korean)
  15. Lee, K.S., Lee, D.E., Jung, S.H., and Lee, G.H. 2018. Analysis of large-scale flood inundation area suing optimal topographic factors. Journal of Korea Water Resources Association 51: 481-490. (in Korean) https://doi.org/10.3741/JKWRA.2018.51.6.481
  16. Lee, S.M., Park, K.D., and Kim, I.K. 2020. Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong river (focusing on water quality and quantity factors). Journal of Korean Society of Water and Wastewater 34: 277-288. (in Korean) https://doi.org/10.11001/jksww.2020.34.4.277
  17. Li, X., Yan, D., Wang, K., Weng, B. Qin, T., and Liu, S. 2019. Flood risk assessment of global watersheds based on multiple machine learning models. Water 2019: 1654-1672.
  18. Moore, I.D., Grason, R.B., and Ladson, A.R. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes 5: 3-30. https://doi.org/10.1002/hyp.3360050103
  19. Oh, M.K., Lee, D.R., Kwon, H.H., and Kim, D.K. 2016. Development of flood inundation area GIS database for Samsung-1 drainage sector, Seoul, Korea. Journal of Korea Water Resources Association 49: 981-993. (in Korean) https://doi.org/10.3741/JKWRA.2016.49.12.981
  20. Rizeei, H.M., Pradhan, B., and Saharkhiz, M.A. 2019. An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimizationbased random forest approaches in GIS. Complex and Intelligent Systems 5: 283-302. https://doi.org/10.1007/s40747-018-0078-8
  21. Seoul Metropolitan City. 2016. Comprehensive plans for the reduction of damage from storm and flood. (in Korean)
  22. Simundic, A.M. 2012. Diagnostic accuracy-part1, Basic concepts: sensitivity and specificity, ROC analysis, STARD statement. Point of Care: The Journal of Near-Patient Testing & Technology 11: 6-8. https://doi.org/10.1097/POC.0b013e318246a5d6
  23. Wang, Z. Lai, C., Chen, X., Yang, B., Zhao, S., and Bai, X. 2015. Flood hazard risk assessment model based on random forest. Journal of Hydrology 527: 1130-1141. https://doi.org/10.1016/j.jhydrol.2015.06.008
  24. Ying, G.S., Maguire, M., Quinn, G., Kulp, M.T., Cyert, L., and Vision in Preschoolers Study Group. 2011. ROC analysis of the accuracy of Noncycloplegic Retinoscopy, Retinomax Autorefractor, and SureSight Vision Screener for preschool vision screening." Investigative Ophthalmology and Visual Science 52: 9658-9664. https://doi.org/10.1167/iovs.11-8559