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Natural Hazards

, Volume 92, Issue 1, pp 173–187 | Cite as

Assessment of world disaster severity processed by Gaussian blur based on large historical data: casualties as an evaluating indicator

  • N. Zhang
  • H. Huang
Original Paper

Abstract

Natural and man-made disasters seriously threaten human life. Knowledge about the severity of disasters in general, and the disaster severity of individual countries, is useful in helping to reduce loss of life and economic losses caused by these disasters. In this paper, we provide a Gaussian blur-based method to calculate the average severity of disasters, instead of using the mean or median values as the average severity. This method can partly eliminate the right skewing that is a result of few serious disasters and the left skewing resulting from a great number of small disasters. A new definition of severity based on a natural logarithm is put forward to quantify the severity of all disasters. Droughts, extreme temperatures, and earthquakes are the top three disasters with the highest severity values. Storms have the highest uncertainty, although their severity is low. After analyzing the hazards of countries, China, Indonesia, India, and America were found to be the four highest hazard countries in the world. Finally, we established an annual disaster hazard value per unit area (Harea) to represent the severity of disasters of countries, taking into account the country’s area. Island countries naturally have high Harea, while most of the other high-Harea countries lie in Africa.

Keywords

Hazard Disaster severity Gaussian blur Risk assessment Big data World disasters 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71741023, 71774093).

References

  1. Barbier EB (2015) Policy: Hurricane Katrina’s lessons for the world. Nature 524(7565):285–287CrossRefGoogle Scholar
  2. Chatterjee C, Mozumder P (2014) Understanding household preferences for hurricane risk mitigation information: evidence from survey responses. Risk Anal 34(6):984–996CrossRefGoogle Scholar
  3. Chen KT, Tsai KJ, Shieh CL (2013) The sediment disaster risk evaluation on the occurrence of debris flow at Taimali Watershed in Taitung County, Taiwan. Appl Mech Mater 405–408:2320–2324CrossRefGoogle Scholar
  4. Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Q 84(2):242–261CrossRefGoogle Scholar
  5. Deryugina T, Kawano L, Levitt, S (2014) The economic impact of hurricane katrina on its victims: evidence from individual tax returns (No. w20713). National Bureau of Economic Research. http://www.nber.org/papers/w20713.pdf
  6. Downing TE, Butterfield R, Cohen S, Huq S, Moss R et al (2001) UNEP vulnerability indices: climate change impacts and adaptation. Med Chir Trans 3(3):1718–1723Google Scholar
  7. Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) A social vulnerability index for disaster management. J Homel Secur Emerg Manag 8(1):1–22Google Scholar
  8. Gedraite ES, Hadad M (2011) Investigation on the effect of a Gaussian Blur in image filtering and segmentation. In: ELMAR Proceedings, pp 393–396Google Scholar
  9. Guha-Sapir D, Vos F, Below R, Ponserre S (2012) Annual disaster statistical review 2011: the numbers and trends (No. CRED/IRSS). UCL, LondonGoogle Scholar
  10. Hallegatte S, Green C, Nicholls RJ, Corfee-Morlot J (2013) Future flood losses in major coastal cities. Nat Clim Change 3(9):802–806CrossRefGoogle Scholar
  11. Homma Y, Watari T, Baba T, Suzuki M (2016) Injury patterns after the landslide disaster in Oshima, Tokyo, Japan on October 16, 2013. Disaster Med Public Health Prep 10(2):248–252CrossRefGoogle Scholar
  12. IPCC (2011) A contribution of working group ii to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  13. Kant Sharma L, Kanga S, Singh Nathawat M, Sinha S, Pandey PC (2012) Fuzzy AHP for forest fire risk modeling. Disaster Prev Manag 21(2):160–171CrossRefGoogle Scholar
  14. Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529(7584):84–87CrossRefGoogle Scholar
  15. Lim CL, Paramesran R, Jassim WA, Yu YP, Ngi Ngan K (2016) Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude. J Franklin Inst 353(17):4715–4733CrossRefGoogle Scholar
  16. Liu B, Siu YL, Mitchell G, Xu W (2013) Exceedance probability of multiple natural hazards: risk assessment in China’s Yangtze River Delta. Nat Hazards 69(3):2039–2055CrossRefGoogle Scholar
  17. Ma Y, Egodawatta P, Mcgree J, Liu A, Goonetilleke A (2016) Human health risk assessment of heavy metals in urban stormwater. Sci Total Environ 557–558:764–772CrossRefGoogle Scholar
  18. Mansoor AB, Anwar A, Khan S (2013) Subjective evaluation of image quality measures for white noise and Gaussian blur-distorted images. Imaging Sci J 61(1):13–21CrossRefGoogle Scholar
  19. Merz B, Aerts J, Arnbjergnielsen K, Baldi M, Becker A, Bichet A et al (2014) Floods and climate: emerging perspectives for flood risk assessment and management. Nat Hazards Earth Syst Sci 2(2):1559–1612CrossRefGoogle Scholar
  20. Morgan MG, Henrion M (1992) Uncertainty—a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, CambridgeGoogle Scholar
  21. Parsons T, Ji C, Kirby E (2008) Stress changes from the 2008 Wenchuan earthquake and increased hazard in the Sichuan basin. Nature 454(7203):509–510CrossRefGoogle Scholar
  22. Remo JWF, Pinter N, Mahgoub M (2016) Assessing Illinois’s flood vulnerability using Hazus-MH. Nat Hazards 81(1):265–287CrossRefGoogle Scholar
  23. Shook G (1997) An assessment of disaster risk and its management in Thailand. Disasters 21(1):77–88CrossRefGoogle Scholar
  24. Thompson KM, Odahowski CL (2016) The costs and valuation of health impacts of measles and rubella risk management policies. Risk Anal 36(7):1357–1382CrossRefGoogle Scholar
  25. Tsai CH, Chen CW (2010) An earthquake disaster management mechanism based on risk assessment information for the tourism industry—a case study from the island of Taiwan. Tour Manag 31(4):470–481CrossRefGoogle Scholar
  26. UN/ISDR (2004) Living with risk: a global review of disaster reduction initiatives 2004 version. United Nations Publication, GenevaGoogle Scholar
  27. UNDRO (1991) Mitigating natural disasters. Phenomena, effects and options. A manual for policy makers and planners. UNDRO/MND/1990 Manual, GenfGoogle Scholar
  28. Wu S, Wu CY, Li SM, Luo XX (2013) Hazard Assessment of landslide disaster in Wenchuan County based on root factor contributing weight model. J Southwest Univ Sci Technol 28(3):28–34Google Scholar
  29. Zhang N, Huang H (2013) Social vulnerability for public safety: a case study of Beijing. Chin Sci Bull 58(19):2387–2394CrossRefGoogle Scholar
  30. Zhang N, Huang H (2017) Resilience analysis of countries under disasters based on multi-source data. Risk Anal.  https://doi.org/10.1111/risa.12807 Google Scholar
  31. Zhang L, Liu X, Li Y, Liu Y, Liu Z, Lin J et al (2012) Emergency medical rescue efforts after a major earthquake: lessons from the 2008 Wenchuan earthquake. Lancet 379(9818):853–861CrossRefGoogle Scholar
  32. Zhang N, Huang H, Su B, Zhao J, Zhang B (2014) Information dissemination analysis of different media towards the application for disaster pre-warning. PLoS ONE 9(5):e98649CrossRefGoogle Scholar
  33. Zhang N, Huang H, Su B (2016) Comprehensive analysis of information dissemination in disasters. Physica A 462:846–857CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Public Safety Research, Department of Engineering PhysicsTsinghua UniversityBeijingChina
  2. 2.Department of Mechanical EngineeringThe University of Hong KongHong Kong SARChina

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