Top > JDR > Multivariate Flood Loss Estimation of the 2018 Bago Flood in M ...

single-dr.php

JDR Vol.15 No.3 pp. 300-311
(2020)
doi: 10.20965/jdr.2020.p0300

Paper:

Views over last 60 days: 975

Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar

Win Win Zin*1,†, Akiyuki Kawasaki*2, Georg Hörmann*3, Ralph Allen Acierto*4, Zin Mar Lar Tin San*1, and Aye Myat Thu*1

*1Department of Civil Engineering, Yangon Technological University
Gyogone, Insein Road, Yangon 11011, Myanmar

†Corresponding author

*2Department of Civil Engineering, The University of Tokyo, Tokyo, Japan

*3Department of Hydrology and Water Resources Management, Kiel University, Kiel, Germany

*4Institute of Industrial Sciences, The University of Tokyo, Tokyo, Japan

Received:
August 1, 2019
Accepted:
January 21, 2020
Published:
March 30, 2020
Creative Commons License
Keywords:
flood loss model, Bago, stepwise regression, Akaike’s information criterion (AIC), best subset regression
Abstract

Flood loss models are essential tools for assessing flood risk. Flood damage assessment provides decision makers with critical information to manage flood hazards. This paper presents a multivariable flood damage assessment based on data from residential building and content damage from the Bago flood event of July 2018. This study aims to identify the influences on building and content losses. We developed a regression-based flood loss estimation model, which incorporates factors such as water depth, flood duration, building material, building age, building condition, number of stories, and floor level. Regression approaches, such as stepwise and best subset regression, were used to create the flood damage model. The selection was based on Akaike’s information criterion (AIC). We found that water depth, flood duration, and building material were the most significant factors determining flood damage in the residential sector.

Cite this article as:
W. Zin, A. Kawasaki, G. Hörmann, R. Acierto, Z. San, and A. Thu, “Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar,” J. Disaster Res., Vol.15 No.3, pp. 300-311, 2020.
Data files:
References
  1. [1] B. Merz, H. Kreibich, R. Schwarze, and A. Thieken, “Review Article “Assessment of economic flood damage”,” Nat. Hazards Earth Syst. Sci., Vol.10, pp. 1697-1724, doi: 10.5194/nhess-10-1697-2010, 2010.
  2. [2] H. G. Wind, T. M. Nierop, C. J. de Blois, and J. L. de Kok, “Analysis of flood damages from the 1993 and 1995 Meuse Floods,” Water Resour. Res., Vol.35, No.11, pp. 3459-3465, doi: 10.1029/1999WR900192, 1999.
  3. [3] G. Zhai, T. Fukuzono, and S. Ikeda, “Modeling flood damage: Case of Tokai flood 2000,” J. Am. Water Resour. Assoc., Vol.41, No.1, pp. 77-92, 2005.
  4. [4] A. H. Thieken, M. Müller, H. Kreibich, and B. Merz, “Flood damage and influencing factors: New insights from the August 2002 flood in Germany,” Water Resour. Res., Vol.41, No.12, doi: 10.1029/2005WR004177, 2005.
  5. [5] A. H. Thieken, A. Olschewski, H. Kreibich, S. Kobsch, and B. Merz, “Development and evaluation of FLEMOps – A new flood loss estimation model for the private sector,” WIT Trans. Ecol. Envir., Vol.118, pp. 315-324, 2008.
  6. [6] H. Kreibich, I. Seifert, B. Merz, and A. H. Thieken, “Develop ment of FLEMOcs – a new model for the estimation of flood losses in the commercial sector,” Hydrolog. Sci. J., Vol.55, No.8, pp. 1302-1314, doi: 10.1080/02626667.2010.529815, 2010.
  7. [7] B. Merz, H. Kreibich, and U. Lall, “Multi-variate flood damage assessment: a tree-based data-mining approach,” Nat. Hazards Earth Syst. Sci., Vol.13, No.1, pp. 53-64, doi: 10.5194/nhess-13-53-2013, 2013.
  8. [8] M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis, “Decision-tree analysis of factors influencing rainfall-related building structure and content damage,” Nat. Hazards Earth Syst. Sci., Vol.14, No.9, pp. 2531-2547, doi: 10.5194/nhess-14-2531-2014, 2014.
  9. [9] D. T. Chinh, A. K. Gain, N. V. Dung, D. Haase, and H. Kreibich, “Multi-variate analyses of flood loss in Can Tho city, Mekong delta,” Water, Vol.8, No.1, pp. 1-21, doi: 10.3390/w8010006, 2016.
  10. [10] R. Hasanzadeh Nafari, T. Ngo, and W. Lehman, “Calibration and validation of FLFArs – A new flood loss function for Australian residential structures,” Nat. Hazards Earth Syst. Sci., Vol.16, No.1, pp. 15-27, doi: 10.5194/nhess-16-15-2016, 2016.
  11. [11] R. Hasanzadeh Nafari, M. Amadio, T. Ngo, and J. Mysiak, “Flood loss modelling with FLF-IT: A new flood loss function for Italian residential structures,” Nat. Hazards Earth Syst. Sci., Vol.17, No.7, pp. 1047-1059, doi: 10.5194/nhess-17-1047-2017, 2017.
  12. [12] P. Wijayanti, X. Zhu, P. Hellegers, Y. Budiyono, and E. C. Van Ireland, “Estimation of river flood damages in Jakarta, Indonesia,” Nat. Hazards, Vol.86, No.3, pp. 1059-1079, doi: 10.1007/s11069-016-2730-1, 2017.
  13. [13] H. Kreibich, A. Botto, B. Merz, and K. Schröter, “Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO,” Risk Anal., Vol.37, No.4, pp. 774-787, doi: 10.1111/risa.12650, 2017.
  14. [14] D. Wagenaar, J. de Jong, and L. M. Bouwer, “Multi-variable flood damage modelling with limited data using supervised learning approaches,” Nat. Hazards Earth Syst. Sci., Vol.17, No.9, pp. 1683-1696, 2017.
  15. [15] F. Carisi, K. Schröter, A. Domeneghetti, H. Kreibich, and A. Castellarin, “Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy),” Nat. Hazards Earth Syst. Sci., Vol.18, No.7, pp. 2057-2079, doi: 10.5194/nhess-18-2057-2018, 2018.
  16. [16] K. Schröter, S. Lüdtke, K. Vogel, H. Kreibich, and B. Merz, “Tracing the value of data for flood loss modelling,” FLOODrisk 2016 – 3rd European Conf. on Flood Risk Management, Article No.05005, doi: 10.1051/e3sconf/20160705005, 2016.
  17. [17] S. Win, W. W. Zin, A. Kawasaki, and Z. M. L. T. San. “Establishment of flood damage function models: A case study in the Bago River Basin, Myanmar,” Int. J. of Disaster Risk Reduction, Vol.28, pp. 688-700, doi: 10.1016/j.ijdrr.2018.01.030, 2018.
  18. [18] W. W. Zin, A. Kawasaki, and S. Win, “River flood inundation mapping in the Bago River Basin, Myanmar,” Hydrological Research Letters, Vol.9, No.4, pp. 97-102, 2015.
  19. [19] A. Kawasaki, N. Ichihara, Y. Ochii, R. A. Acierto, A. Kodaka, and W. W. Zin, “Disaster response and river infrastructure management during the 2015 Myanmar floods: a case in the Bago River Basin,” Int. J. of Disaster Risk Reduction, Vol.24, pp. 151-159, 2017.
  20. [20] W. W. Zin, A. Kawasaki, W. Takeuchi, Z. M. L. T. San, K. Z. Htun, T. H. Aye, and S. Win, “Flood Hazard Assessment of Bago River Basin, Myanmar,” J. Disaster Res., Vol.13, No.1, pp. 14-21, 2018.
  21. [21] S. Weisberg, “Applied linear regression,” 2nd Edition, John Wiley & Sons, 1985.
  22. [22] R. A. Gordon, “Issues in multiple regression,” American J. of Sociology, Vol.73, No.5, pp. 592-616, 1968.
  23. [23] P. Ruengvirayudh and G. P. Brooks, “Comparing stepwise regression models to the best-subsets models, or, the art of stepwise,” General Linear Model J., Vol.42, No.1, pp. 1-14, 2016.
  24. [24] H. Akaike, “Information theory as an extension of the maximum likelihood principle,” 2nd Int. Symp. on Information Theory, pp. 267-281, 1973.
  25. [25] S. Chatterjee and J. S. Simonoff, “Handbook of Regression Analysis,” John Wiley & Sons, 2013.
  26. [26] X. Yan and X. G. Su, “Linear Regression Analysis,” World Scientific Publishing Co. Pte. Ltd., 2009.
  27. [27] R Core Team, “R: A Language and Environment for Statistical Computing,” R Foundation for Statistical Computing, 2019, https://www.R-project.org [accessed May 1, 2019]

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024