All Issue

2018 Vol.38, Issue 6 Preview Page
December 2018. pp. 839-850
The flood damage in urban areas due to torrential rain is increasing with urbanization. For this reason, accurate and rapid flooding forecasting and expected inundation maps are needed. Predicting the extent of flooding for certain rainfalls is a very important issue in preparing flood in advance. Recently, government agencies are trying to provide expected inundation maps to the public. However, there is a lack of quantifying the extent of inundation caused by a particular rainfall scenario and the real-time prediction method for flood extent within a short time. Therefore the real-time prediction of flood extent is needed based on rainfall-runoff-inundation analysis. One/two dimensional model are continued to analyize drainage network, manhole overflow and inundation propagation by rainfall condition. By applying the various rainfall scenarios considering rainfall duration/distribution and return periods, the inundation volume and depth can be estimated and stored on a database. The Rainfall-Duration-Flooding Quantity (RDF) relationship curve based on the hydraulic analysis results and the Self-Organizing Map (SOM) that conducts unsupervised learning are applied to predict flooded area with particular rainfall condition. The validity of the proposed methodology was examined by comparing the results of the expected flood map with the 2-dimensional hydraulic model. Based on the result of the study, it is judged that this methodology will be useful to provide an unknown flood map according to medium-sized rainfall or frequency scenario. Furthermore, it will be used as a fundamental data for flood forecast by establishing the RDF curve which the relationship of rainfall-outflow-flood is considered and the database of expected inundation maps.
집중호우에 의한 도시 유역의 침수 피해가 도시화에 따라 증가하는 추세이며, 이에 따라 정확하면서도 신속한 홍수예보 및 침수 예상도 표출이 필요하다. 특정 강우량에 따른 미지의 침수 범위를 예상하는 것은 도시 유역의 홍수에 대한 사전 대비에 매우 중요한 사안이며, 이를 위해 현재 홍수 예보와 관련된 정부기관에서 침수 피해 예상도를 주민들에게 제공하고자 하고 있다. 하지만, 특정 강우에 따른 정확한 침수 범위를 정량화 하여 표출하는데 부족함이 있으며, 강우량과 지속시간에 따른 홍수의 크기에 대한 분석을 실시하고 수리학적 연계를 통한 준 실시간 침수범위 표출 방안을 고찰해야할 시기이다. 제시된 물리적 해석기반 자료를 이용하여 강우량-지속시간-침수량 관계곡선(Rainfall-Duration-Flooding quantity relationship curve, RDF)을 제시하고, 자율학습을 수행하는 자기조직화 특징 지도와 연계하여 미지의 침수 지도를 예측하였다. 예측한 침수 지도와 2차원 침수모형을 통한 결과를 비교하여, 제시된 방법론의 타당성을 검토하였다. 연구 결과를 통하여 중규모의 강우량 또는 빈도의 사상에 따른 미지의 침수범위를 제시하는데 용이할 것으로 판단된다. 더욱이 다양한 강우-월류량-홍수 양상을 내포하는 RDF 관계 곡선과 최적 침수예상도 데이터베이스를 구축함으로서 추후에 홍수예보의 기초자료로서 사용될 것이다.
  1. Bae, D. H., Shim, J. B. and Yoon, S. S. (2012). “Development and assesment of flow nomograph for the real-time flood forecasting in Cheonggye stream.” Journal Korea Water Resour. Assoc., Vol. 45, No. 11, pp. 1107-1119 (in Korean).
  2. Chang, F. J., Chang, L. C. and Wang, Y. S. (2007). “Enforced self-organizing map neural networks for river flood forecasting.” Hydrological Process, Vol. 21, pp. 741-749.
  3. Choi, H. G., Han, K. Y., Roh, H. S. and Park, S. J. (2013). “Comparison of data-based real-time flood forecasting model.” Journal Korean Society of Civil Engineers, Vol. 33, No. 5, pp. 1809-1827 (in Korean).
  4. Choi, S. M., Yoon, S. S. and Choi, Y. J. (2015). “Evaluation of high-resolution QPE data for urban runoff analysis.” Journal Korea Water Resour. Assoc., Vol. 48, pp. 719-728 (in Korean).
  5. Han, K. Y. (2012). Flood Disaster Mitigation. R&D Press.
  6. Huber, W. C. and Dickson, R. E. (1988). Storm Water Management Model. User’s Manual Ver. 4, U.S. EPA.
  7. Jang, S. H., Yoon, J. Y. and Yoon, Y. N. (2006). “A study on the improvement of Huff’s method in Korea: I. Review of applicability of Huff’s method in Korea.” Journal Korea Water Resour. Assoc, Vol. 39, pp. 767-777, doi:10.3741/JKWRA.2006.39.9.767 (in Korean with Englishabstract).
  8. Jo, D. J. and Jeon, B. H. (2014). “Development of flood nomograph for inundation forecasting in urban districts.” Journal of KOSHAM, Vol. 13, No. 3, pp. 37-42 (in Korean).
  9. Kalteh, A. M., Hjorth, P. and Berndtsson, R. (2008). “Review of the self-organizing map (SOM) approach in water resources : Analysis, modeling and application.” Elsevier Environmental Modelling & software, Vol. 23, pp. 835-845.
  10. Keum, H. J., Kim, H. I. and Han, K. Y. (2018). “Real-TIME forecast of rainfall impact on urban inundation.” Journal of the Korean Association of Geographic Information Studies, Vol. 21, No. 3, pp. 76-91.
  11. Kim, H. I., Keum, H. J. and Han, K. Y. (2018). “Application and comparison of dynamic artificial neural networks for urban inundation analysis.” Journal of the Korea Society of Civil Engineers, Vol. 38, No. 5, pp. 671-684 (in Korean).
  12. Lee, B. H. (2006). “A study on the characteristics and composition direction of urban flood control system.” Water and Future, pp. 50-54.
  13. Lopez, M., Valero, S., Senabre, C., Aparichi, J. and Gabaldon, A. (2012). “Application of SOM neural networks to short-term load forecasting : The Spanish electricity market case study.” Elsevier Electric Power Systems Research, Vol. 91, pp. 18-27.
  14. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. and Veith, T. L. (2007). “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.” ASABE., Vol. 50, No. 3, pp. 885-900.
  15. Nanda, T., Sahoo, B. and Chatterjee, C. (2017). “Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data.” Journal of Hydrology, Vol. 549, pp. 133-147.
  16. Seoul Metropolitan City (2015). Comprehensive Plan for Storm and Flood Damage Reduction (in Korean).
  17. Shin, S. Y., Yeo, C. G., Baek, C. H. and Kim, Y. J. (2005). “Mapping inundation areas by flash flood and developing rainfall standards for evacuation in urban settings.” Journal of the Korean Association of Geographic Information Studies, Vol. 8, No. 4, pp. 71-80 (in Korean).
  18. Tsai, M. H., Sung, E. X. and Kang, S. C. (2016). “Data-driven flood analysis and decision support.” Nat. Hazards Eearth Syst, Sci. Discuss., doi:10.5194/nhess-2016-141.
  19. Wu, M. C., Lin, G. F. and Lin, H. Y. (2014). “Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map.” Hydrological Process, Vol. 28, pp. 386-397.
  • Publisher :Korean Society of Civil Engineers
  • Publisher(Ko) :대한토목학회
  • Journal Title(Ko) :대한토목학회 논문집
  • Volume : 38
  • No :6
  • Pages :839-850