DOI QR코드

DOI QR Code

지역별 홍수피해추정을 위한 강우특성에 대한 홍수피해자료의 회귀모형

Regression models on flood damage records by rainfall characteristics for regional flood damage estimates

  • 임연택 (영남대학교 건설시스템공학과) ;
  • 최현일 (영남대학교 건설시스템공학과)
  • Lim, Yeon Taek (Department of Civil Engineering, Yeungnam University) ;
  • Choi, Hyun Il (Department of Civil Engineering, Yeungnam University)
  • 투고 : 2020.10.26
  • 심사 : 2020.11.10
  • 발행 : 2020.11.30

초록

기후변화로 인해 홍수의 빈도와 강도가 증가하고 있으므로, 구조적인 방법만으로 홍수피해에 대처하기에는 한계가 있다. 따라서, 장래 홍수피해 예측을 위해 과거 홍수피해 자료를 수집하고 분석하는 것은 비구조적인 홍수대책으로 필요한 요소 중 하나이다. 본 논문에서는 지리적, 기후적 영향으로 심각한 홍수피해가 빈번하게 발생하고 있는 경상북도 지역의 홍수피해추정을 위해, 홍수피해의 주요 발생원인 중 하나인 강우특성에 대한 최근 20년(1999-2018) 동안 홍수피해자료의 회귀분석을 실시하였다. 또한, 울릉군을 제외한 22개 경상북도 시군별로 제시된 지역회귀함수의 강우특성과 지형특징과의 관계를 분석하고, 각 시군의 100년 빈도 강우량에 대한 홍수피해 위험도를 추정하였다. 홍수피해 추정결과, 경상북도에서는 동해안에 인접한 군지역에서 상대적으로 높은 피해위험도가 예측되었다. 본 논문에서 개발된 지역 피해추정함수는 계획 또는 예보 강우량에 대한 홍수피해 위험도를 산정하는 비구조적 대책 중 하나로 사용될 수 있을 것으로 기대한다.

There are limitations to cope with flood damage by structural strategies alone because both frequency and intensity of floods are increasing due to climate change. Therefore, it is one of the necessary factors in the nonstructural countermeasures to collect and analyze historical flood damage records for the future flood damage assessments. In order to estimate flood damage costs in Gyeongsangbuk-do where severe flood damage occurs frequently due to geographical and climatic effects, this paper has performed the regression analysis on flood damage records over the past 20 years (1999-2018) by rainfall characteristics, which is one of the major causes of flood damage. This paper has then examined the relationship between the terrain features and rainfall characteristics in the regional regression functions, and also estimated the flood damage risk for 100-year rainfall by using the regional regression functions presented for the 22 administrative districts in Gyeongsangbuk-do excluding Ulleung-gun. The flood damage assessment shows that the relatively high damage risk is estimated for county areas adjacent to the eastern coast in Gyeongsangbuk-do. The regional damage estimate functions in this paper are expected to be used as one of the nonstructural countermeasures to estimate flood damage risk for the design or forecasting rainfall data.

키워드

참고문헌

  1. Belsley, D.A., Kuh, E., and Welsch, R.E. (1980) Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, John Wiley & Sons.
  2. Bentler, P.M., and Chou, C.P. (1987) Practical issues in structural modeling, Sociological Methods and Research, Vol. 16, No. 1, pp. 78-117. https://doi.org/10.1177/0049124187016001004
  3. Bollen, K.A., and Jackman, R.W. (1985) Regression diagnostics: An expository treatment of outliers and influential cases, Sociological Methods & Research, Vol. 13 No. 4 pp. 510-542. https://doi.org/10.1177/0049124185013004004
  4. Chang, L.F., Lin, C.H., amd Su, M.D. (2008) Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation, Water SA, Vol. 34, No. 2, pp. 209-216. https://doi.org/10.4314/wsa.v34i2.183641
  5. Dancey, C.P. and Reidy, J. (2011) Statistics without Maths for Psychology, 5th Ed., Prentice Hall, p.175
  6. Cook, R.D. (1977) Detection of Influential Observation in Linear Regression, Technometrics, Vol. 19, No. 1, pp. 15-18. https://doi.org/10.2307/1268249
  7. Cortes, M., Turco, M., Llasat-Botija, M., and Llasat, M.C. (2018) The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain), Natural Hazards and Earth System Sciences, Vol. 18, No. 3, pp. 857-868. https://doi.org/10.5194/nhess-18-857-2018
  8. EM-DAT (2020) The International Disaster Database, Retrieved from https://www.emdat.be/.
  9. Fox, J. (2008) Applied regression analysis and generalized linear models, 2nd ed, Thousand Oaks, CA: Sage.
  10. Green, S.B. (1991) How many subjects it take to do a regression analysis?, Multivariate Behavioral Research, Vol. 26, No. 3, pp. 499-510. https://doi.org/10.1207/s15327906mbr2603_7
  11. Halinski, R.S., and Feldt, L.S. (1970) The selection of variables in multiple regression analysis, Journal of Educational Measurement, Vol. 7, No. 3, pp. 151-157. https://doi.org/10.1111/j.1745-3984.1970.tb00709.x
  12. Harris, R.J. (1975) Primer of Multivariate Statistics, Academic Press.
  13. Hoaglin, D.C., and Welsch, R.E. (1978) The hat matrix in regression and ANOVA, The American Statistician, Vol. 32, No. 1, pp. 17-22. https://doi.org/10.2307/2683469
  14. IPCC (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability.
  15. Jang, O.J., and Kim, Y.O. (2009) Flood Risk Estimation Using Regional Regression Analysis, Journal of the Korean Society of Hazard Mitigation, Vol. 9, No. 4, pp. 71-80.
  16. Kim, J.S., Choi, C.H., Lee, J. S., and Kim, H.S. (2017) Damage Prediction Using Heavy Rain Risk Assessment: (2) Development of Heavy Rain Damage Prediction Function, Journal of the Korean Society of Hazard Mitigation, Vol. 17, No. 2, pp. 361-370. https://doi.org/10.9798/KOSHAM.2017.17.2.361
  17. KMA (2020) Korean Climate Change Assessment Report 2020.
  18. Lee, H.J., Ryu, S.H., Won, S.H., Jo, E.J., Kim, S.W., and Joe, G.H. (2016) A Study on Model of Heavy Rain Risk Prediction Using Influencing Factors of Flood Damage, Journal of the Korean Society of Hazard Mitigation, Vol. 16, No. 3, pp. 39-45. https://doi.org/10.9798/KOSHAM.2016.16.3.39
  19. MOIS (2018) Statistical yearbook of natural disaster 2018.
  20. Miller, D.E., and Kunce, J.T. (1973) Prediction and statistical overkill revisited, Measurement and Evaluation in Guidance, Vol. 6, No. 3, pp. 157-163. https://doi.org/10.1080/00256307.1973.12022590
  21. Moore, D.S., Notz, W.I., and Flinger, M.A. (2013) The basic practice of statistics (6th ed.), W. H. Freeman and Company.
  22. Nunnally, J.C., and Bernstein, I.H. (1994) Psychometric Theory : 3rd edition, Mc Graw-Hill.
  23. Oak, Y.S., Jeong, M.S., Lee, Y.K., and Lee, C.H. (2017) A Study on the Estimation of Flood Damage Using Frequency Analysis in Gyeongbuk Province, Journal of the Korean Society of Hazard Mitigation, Vol. 17, No. 2, pp. 277-286. https://doi.org/10.9798/KOSHAM.2017.17.2.277
  24. Park, D.H., Ahn, J.H., and Choi, Y.J. (2011) Correlation between Storm Characteristics and Flood Damage, Korean Wetlands Society, Vol. 13, No. 2, pp. 219-229.
  25. Spekkers, M.H., Kok, M., Clemens, F.H.L.R., and ten Veldhuis, J.A.E. (2013) A statistical analysis of insurance damage claims related to rainfall extremes, Hydrology and Earth System Sciences, Vol. 17, No. 3, pp. 913-922. https://doi.org/10.5194/hess-17-913-2013
  26. Spekkers, M.H., Kok, M., Clemens, F.H.L. R., and ten Veldhuis, J.A.E. (2014) Decision-tree analysis of factors influencing rainfall-related building structure and content damage, Natural hazards and earth system sciences, Vol. 14, No. 9, pp. 2531-2547. https://doi.org/10.5194/nhess-14-2531-2014
  27. Suzuki, N., Olson , D.H., and Reilly, E.C. (2007) Developing landscape habitat models for rare amphibians with small gegraphic ranges : A case study of Siskiyou Mountains salamanders in the western USA, Biodiversity and Conservation, Vol. 17, No. 9, pp. 2197-2218. https://doi.org/10.1007/s10531-007-9281-4