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JDR Vol.15 No.7 pp. 822-832
(2020)
doi: 10.20965/jdr.2020.p0822

Paper:

Statistical Analysis of Building Damage from the 2013 Super Typhoon Haiyan and its Storm Surge in the Philippines

Tanaporn Chaivutitorn*1, Thawalrat Tanasakcharoen*2, Natt Leelawat*2,*3,†, Jing Tang*3,*4, Carl Vincent C. Caro*5, Alfredo Mahar Francisco A. Lagmay*6, Anawat Suppasri*7, Jeremy D. Bricker*8, Volker Roeber*9, Carine J. Yi*10, and Fumihiko Imamura*7

*1School of Management, National Taiwan University of Science and Technology
43, Sec. 4, Keelung Road, Taipei 106, Taiwan

*2Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

Corresponding author

*3Disaster and Risk Management Information Systems Research Group, Chulalongkorn University, Bangkok, Thailand

*4International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

*5Viosimos Integrated Planning Consultants Inc., Bagumbong Caloocan City, The Philippines

*6University of the Philippines Diliman, Manila, The Philippines

*7International Research Institute of Disaster Science (IRIDeS), Tohoku University, Miyagi, Japan

*8Department of Hydraulic Engineering, Delft University of Technology, Delft, The Netherlands

*9Université de Pau et des Pays de l’Adour, Anglet, France

*10R. Park & Associates Inc., Ontario, Canada

Received:
May 18, 2020
Accepted:
July 14, 2020
Published:
December 1, 2020
Keywords:
building damage, statistical analysis, storm surge, Super Typhoon Haiyan
Abstract

In November 2013, Super Typhoon Haiyan (Yolanda) hit the Philippines. It caused heavy loss of lives and extensive damages to buildings and infrastructure. When collapsed buildings are focused on, it is interesting to find that these buildings did not collapse for the same reasons after the landfall of the typhoon and storm surge. The objective of this study is to develop a statistical model for building damage due to Super Typhoon Haiyan and its storm surge. The data were collected in collaboration with Tanauan Municipality, the Philippines. The data for the inundation map were obtained by field surveys conducted on-site to determine the cause of the damages inferred from satellite data. The maximum wind speed was derived from the Holland parametric hurricane model based on the Japan Meteorological Agency (JMA) typhoon track data and the inundation depth of storm surge was calculated using the MIKE model. Multinomial logistic regression was used to develop a model to identify the significant factors influencing the damage to buildings. The result of this work is expected to be used to prepare urban plans for preventing damage from future storms.

Cite this article as:
Tanaporn Chaivutitorn, Thawalrat Tanasakcharoen, Natt Leelawat, Jing Tang, Carl Vincent C. Caro, Alfredo Mahar Francisco A. Lagmay, Anawat Suppasri, Jeremy D. Bricker, Volker Roeber, Carine J. Yi, and Fumihiko Imamura, “Statistical Analysis of Building Damage from the 2013 Super Typhoon Haiyan and its Storm Surge in the Philippines,” J. Disaster Res., Vol.15, No.7, pp. 822-832, 2020.
Data files:
References
  1. [1] S. Brown, “The Philippines Is the Most Storm-Exposed Country on Earth,” TIME, November 11, 2013, http://world.time.com/2013/11/11/the-philippines-is-the-most-storm-exposed-country-on-earth [accessed September 16, 2017]
  2. [2] H. Takagi, M. de Leon, M. Esteban, T. Mikami, and R. Nakamura, “Storm surge due to 2013 Typhoon Yolanda (Haiyan) in Leyte Gulf, the Philippines,” M. Esteban, H. Takagi, and T. Shibayama (Eds.), “Handbook of Coastal Disaster Mitigation for Engineers and Planners,” pp. 133-144, Elsevier, 2015.
  3. [3] N. Leelawat, A. Suppasri, S. Kure, C. J. Yi, C. M. R. Mateo, and F. Imamura, “Disaster warning system in the Philippines through enterprise engineering perspective: A study on the 2013 Super Typhoon Haiyan,” J. Disaster Res., Vol.10, No.6, pp. 1041-1050, doi: 10.20965/jdr.2015.p1041, 2015.
  4. [4] H. S. Lee and K. O. Kim, “Storm surge and storm waves modelling due to Typhoon Haiyan in November 2013 with improved dynamic meteorological conditions,” Procedia Engineering, Vol.116, pp. 699-706, 2015.
  5. [5] A. M. F. Lagmay, R. P. Agaton, M. A. C. Bahala, J. B. L. T. Briones, K. M. C. Cabacaba, C. V. C. Caro, L. L. Dasallas, L. A. L. Gonzalo, C. N. Ladiero, J. P. Lapidez, M. T F. Mungcal, J. V. R. Puno, M. M. A. C. Ramos, J. Santiago, J. K. Suarez, and J. P. Tablazon, “Devastating storm surges of Typhoon Haiyan,” Int. J. of Disaster Risk Reduction, Vol.11, pp. 1-12, 2015.
  6. [6] A. Suppasri, C. J. Yi, N. Leelawat, M. Watanabe, J. D. Bricker, and F. Imamura, “Field Survey and Analysis of Damaged School Buildings by the 2013 Typhoon Haiyan and Storm Surge,” J. of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering), Vol.71, No.2, pp. I_1669-I_1674, 2015.
  7. [7] BBC News, “Mapping Typhoon Haiyan,” http://www.bbc.com/news/world-asia-24917722 [accessed September 16, 2017]
  8. [8] Y. Tajima and T. Shimozono, “Super Typhoon Haiyan in the Philippines,” Y. Hayashi, Y. Suzuki, S. Sato, and K. Tsukahara (Eds.) “Disaster Resilient Cities: Concepts and Practical Examples,” pp. 21-29, Elsevier, 2016.
  9. [9] M. Baradaranshoraka, J. P. Pinelli, K. Gurley, X. Peng, and M. Zhao, “Hurricane wind versus storm surge damage in the context of a risk prediction model,” J. of Structural Engineering, Vol.143, No.9, 2017.
  10. [10] S. D. Amoroso and K. R. Gurley, “Response of Structures to wind, storm surge, flood, and waves,” D. B. Peraza, W. L. Coulbourne, and M. Griffith (Eds.), “Engineering Investigations of Hurricane Damage: Wind versus Water,” pp. 62-79, American Society of Civil Engineers, 2014.
  11. [11] Q. Li, C. Wang, and H. Zhang, “A probabilistic framework for hurricane damage assessment considering non-stationarity and correlation in hurricane actions,” Structural Safety, Vol.59, pp. 108-117, 2016.
  12. [12] H. Ham, S. Lee, and H. Kim, “Development of typhoon fragility for industrial buildings,” Proc. of the 7th Asia-Pacific Conf. on Wind Engineering, 2009.
  13. [13] G. Pita, J. P. Pinelli, S. Cocke, K. Gurley, J. Mitrani-Reiser, J. Weekes, and S. Hamid, “Assessment of hurricane-induced internal damage to low-rise buildings in the Florida Public Hurricane Loss Model,” J. of Wind Engineering and Industrial Aerodynamics, Vols.104-106, pp. 76-87, 2012.
  14. [14] Y. M. Dai, X. G. Yan, X. J. Wang, H. X. Sun, and Y. G. Li, “Statistics and Analysis of typhoons landing and failure mechanism of coastal low-rise buildings in China,” Applied Mechanics and Materials, Vols.226-228, pp. 1072-1075, 2012.
  15. [15] J. E. Padgett, A. Spiller, and C. Arnold, “Statistical analysis of coastal bridge vulnerability based on empirical evidence from Hurricane Katrina,” Structure and Infrastructure Engineering, Vol.8, No.6, pp. 595-605, 2012.
  16. [16] J. P. Pinelli, E. Simiu, K. Gurley, C. Subramanian, L. Zhang, A. Cope, J. J. Filliben, and S. Hamid, “Hurricane damage prediction model for residential structures,” J. of Structural Engineering, Vol.130, No.11, pp. 1685-1691, 2004.
  17. [17] K. Nishijima, T. Maruyama, and M. Graf, “A preliminary impact assessment of typhoon wind risk of residential buildings in Japan under future climate change,” Hydrological Research Letters, Vol.6, pp. 23-28, 2012.
  18. [18] T. C. Duy, C. N. Xuan, M. N. Dai, H. N. Huu, and C. B. Tat, “Typhoons and technical solutions recommended for existing and new houses in the cyclonic regions in Vietnam,” Electronic J. of Structural Engineering, Vol.8, Special Issue 2, pp. 8-18, 2008.
  19. [19] A. Suppasri, I. Charvet, J. Macabuag, T. Rossetto, N. Leelawat, P. Latcharote, and F. Imamura, “Building damage assessment and implications for future tsunami fragility estimations,” M. Esteban, H. Takagi, and T. Shibayama (Eds.), “Handbook of Coastal Disaster Mitigation for Engineers and Planners,” pp. 147-178, Elsevier, 2015.
  20. [20] A. Suppasri, P. Latcharote, J. D. Bricker, N. Leelawat, A. Hayashi, K. Tamashita, F. Makinoshima, V. Roeber, and F. Imamura, “Improvement of tsunami countermeasures based on lessons from the 2011 Great East Japan earthquake and tsunami – Situation after five years,” Coastal Engineering J., Vol.58, No.4, 2016.
  21. [21] N. Leelawat, A. Suppasri, I. Charvet, and F. Imamura, “Building damage from the 2011 Great East Japan tsunami: quantitative assessment of influential factors,” Natural Hazards, Vol.73, No.2, pp. 449-471, 2014.
  22. [22] N. Leelawat, A. Suppasri, I. Charvet, T. Kimura, D. Sugawara, and F. Imamura, “A study on influential factors on building damage in Kesennuma, Japan from the 2011 Great East Japan tsunami,” Engineering J., Vol.19, No.3, pp. 105-116, 2015.
  23. [23] W. Treeranurat, K. Saengtubtim, N. Wisittiwong, J. Tang, N. Leelawat, A. Suppasri, K. Pakaksung, and F. Imamura, “Building damage analysis from the 2011 Great East Japan tsunami,” Proc. of the 2019 Int. Conf. on Engineering and Natural Science, pp. 33-39, 2019.
  24. [24] N. Leelawat, A. Suppasri, O. Murao, and F. Imamura, “A study on the influential factors on building damage in Sri Lanka during the 2004 Indian Ocean tsunami,” J. of Earthquake and Tsunami, Vol.10, No.2, Article No.1640001, 2016.
  25. [25] P. Latcharote, N. Leelawat, A. Suppasri, P. Thamarux, and F. Imamura, “Estimation of fatality ratios and investigation of influential factors in the 2011 Great East Japan tsunami,” Int. J. of Disaster Risk Reduction, Vol.29, pp. 37-54, 2018.
  26. [26] P. Latcharote, N. Leelawat, A. Suppasri, and F. Imamura, “Developing estimating equations of fatality ratio based on surveyed data of the 2011 Great East Japan tsunami,” IOP Conf. Series: Earth and Environmental Science, Volume 56, IOP Publishing, 2017.
  27. [27] National Weather Service, http://www.nws.noaa.gov/oh/rfcdev/docs/Final_Report_EvaluationHydraulicModels.pdf [accessed October 16, 2017]
  28. [28] G. A. F. Seber, “Linear Regression Analysis,” Wiley, 1977.
  29. [29] E. Mas, J. D. Bricker, S. Kure, B. Adriano, C. J. Yi, A. Suppasri, and S. Koshimura, “Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines,” Natural Hazards and Earth System Sciences, Vol.15, No.4, pp. 805-816, 2015.
  30. [30] J. D. Bricker, H. Takagi, E. Mas, S. Kure, B. Adriano, C. J. Yi, and V. Roeber, “Spatial variation of damage due to storm surge and waves during Typhoon Haiyan in the Philippines,” J. of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering), Vol.70, No.2, pp. I_231-I_235, 2014.
  31. [31] M. Watanabe, J. D. Bricker, K. Goto, and F. Imamura, “Factors responsible for the limited inland extent of sand deposits on Leyte Island during 2013 Typhoon Haiyan,” J. of Geophysical Research: Oceans, Vol.122, No.4, pp. 2795-2812, 2017.
  32. [32] “City Population,” https://www.citypopulation.de/php/philippines-visayas-admin.php [accessed March 16, 2018]
  33. [33] “Pearson Product-Moment Correlation,” https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php [accessed March 16, 2018]
  34. [34] D. E. Hinkle, W. Wiersma, and S. G. Jurs, “Applied Statistics for the Behavioral Sciences,” 5th edition, Houghton Mifflin, 2003.
  35. [35] D. R. Cox and E. J. Snell, “The Analysis of Binary Data,” Chapman and Hall, 1989.
  36. [36] N. J. Nagelkerke, “A note on a general definition of the coefficient of determination,” Biometrika, Vol.78, No.3, pp. 691-692, 1991.
  37. [37] D. McFadden, “Conditional logit analysis of qualitative choice behavior,” Paul Zarembka (Ed.), “Frontiers in Econometric,” pp. 105-142, Wiley, 1974.
  38. [38] “Sendai Framework for Disaster Risk Reduction 2015–2030,” https://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf [accessed April 1, 2018]

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Last updated on Mar. 05, 2021