Browse > Article
http://dx.doi.org/10.17663/JWR.2020.22.4.302

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

Lim, Yeon Taek (Department of Civil Engineering, Yeungnam University)
Choi, Hyun Il (Department of Civil Engineering, Yeungnam University)
Publication Information
Journal of Wetlands Research / v.22, no.4, 2020 , pp. 302-311 More about this Journal
Abstract
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.
Keywords
Flood Damage; Rainfall Characteristics; Regression Analysis; Damage Estimate;
Citations & Related Records
연도 인용수 순위
  • Reference
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
12 Harris, R.J. (1975) Primer of Multivariate Statistics, Academic Press.
13 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.   DOI
14 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.   DOI
15 IPCC (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability.
16 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.
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.   DOI
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.   DOI
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 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.   DOI
24 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.   DOI
25 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.
26 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.   DOI
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.   DOI