Skip to main content

Analyzing Flood Fatalities in Vietnam Using Statistical Learning Approach and National Disaster Database

  • Chapter
  • First Online:
Resettlement Challenges for Displaced Populations and Refugees

Part of the book series: Sustainable Development Goals Series ((SDGS))

Abstract

Floods and storms have had a severe impact on the people of Vietnam over many years, particularly regarding an unacceptably high death toll. However, it still lacks studies on flood-related fatalities in Vietnam. This research aims to explore flood fatalities on a national scale and analyze damage-influencing attributes related to flood fatalities using the national disaster database of Vietnam and statistical learning approach. Records covering 27 years from 1989 to 2015 indicate at least 14,927 flood mortalities in Vietnam. The analysis results of statistical learning methods show that housing impact factor has the most considerable influence on flood fatalities. The results can provide implications for housing policies for the poor in flood-prone areas. The objective of reduction in mortality in disasters is under Goal 11 of Sustainable Development Goals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ashley, S. T., & Ashley, W. S. (2008). Flood fatalities in the United States. Journal of Applied Meteorology and Climatology, 47(3), 805–818. https://doi.org/10.1175/2007jamc1611.1.

    Article  Google Scholar 

  • Below, R., Vos, F., & Guha-Sapir, D. (2010). Moving towards harmonization of disaster data: A study of six Asian databases. Brussels: Centre for Research on the Epidemiology of Disasters.

    Google Scholar 

  • Bi, J. (2012). A review of statistical methods for determination of relative importance of correlated predictors and identification of drivers of consumer liking. Journal of Sensory Studies, 27(2), 87–101. https://doi.org/10.1111/j.1745-459X.2012.00370.x.

    Article  Google Scholar 

  • Canty, A., & Ripley, B. (2016). Boot: Bootstrap R (S-plus) functions. R package version, 1, 3–18.

    Google Scholar 

  • Coates, L. (1999). Flood fatalities in Australia, 1788-1996. Australian Geographer, 30(3), 391–408. https://doi.org/10.1080/00049189993657.

    Article  Google Scholar 

  • Crichton, D. (2004). Flood risks in the former Grampian Region since Devolution (A Research Report for WWF by David Crichton, Vol. 16 November 2015): WWF Scotland.

    Google Scholar 

  • Di Mauro, M., & de Bruijn, K. M. (2012). Application and validation of mortality functions to assess the consequences of flooding to people. Journal of Flood Risk Management, 5(2), 92–110. https://doi.org/10.1111/j.1753-318X.2011.01131.x.

    Article  Google Scholar 

  • Di Mauro, M., De Bruijn, K. M., & Meloni, M. (2012). Quantitative methods for estimating flood fatalities: Towards the introduction of loss-of-life estimation in the assessment of flood risk. Natural Hazards, 63(2), 1083–1113. https://doi.org/10.1007/s11069-012-0207-4.

    Article  Google Scholar 

  • FitzGerald, G., Du, W., Jamal, A., Clark, M., & Hou, X. Y. (2010). Flood fatalities in contemporary Australia (1997-2008). Emergency Medicine Australasia, 22(2), 180–186. https://doi.org/10.1111/j.1742-6723.2010.01284.x.

    Article  Google Scholar 

  • Grasso, V. F., & Dilley, M. (2013). A comparative review of country-level and regional disaster loss and damage databases. New York: United Nations Development Programme Bureau for Crisis Prevention and Recovery.

    Google Scholar 

  • Grömping, U. (2006). Relative importance for linear regression in R: The package relaimpo. Journal of Statistical Software, 17(1), 1–27. https://doi.org/10.18637/jss.v017.i01.

    Article  Google Scholar 

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Harlow: Pearson Education Limited.

    Google Scholar 

  • Hasanzadeh Nafari, R., Ngo, T., & Mendis, P. (2016). An assessment of the effectiveness of tree-based models for multi-variate flood damage assessment in Australia. Water, 8(7), 282. https://doi.org/10.3390/w8070282.

    Article  Google Scholar 

  • Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., et al. (2013). Global flood risk under climate change. Nature Climate Change, 3(9), 816–821. https://doi.org/10.1038/nclimate1911.

    Article  Google Scholar 

  • Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A., & Van Der Laan, M. J. (2006). Survival ensembles. Biostatistics, 7(3), 355–373.

    Article  Google Scholar 

  • Hughey, E., Bell, H., & Chatman, M. (2011). Who needs what? A case study of post-disaster damage and needs assessment (DANA) in Vietnam. Risk, Hazards & Crisis in Public Policy, 2(4), 1–24. https://doi.org/10.2202/1944-4079.1097.

    Article  Google Scholar 

  • IRDR (2014). IRDR Peril Classification and Hazard Glossary (DATA Project Report No. 1). Beijing: Integrated Research on Disaster Risk (IRDR).

    Google Scholar 

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013a). Introduction. In An introduction to statistical learning: With applications in R (pp. 1–14). New York: Springer New York.

    Chapter  Google Scholar 

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013b). Resampling methods. In An introduction to statistical learning: With applications in R (pp. 175–201). New York: Springer.

    Chapter  Google Scholar 

  • Jonkman, S. N., & Kelman, I. (2005). An analysis of the causes and circumstances of flood disaster deaths. Disasters, 29(1), 75–97. https://doi.org/10.1111/j.0361-3666.2005.00275.x.

    Article  Google Scholar 

  • Jonkman, S. N., Maaskant, B., Boyd, E., & Levitan, M. L. (2009). Loss of life caused by the flooding of New Orleans after hurricane Katrina: Analysis of the relationship between flood characteristics and mortality. Risk Analysis, 29(5), 676–698. https://doi.org/10.1111/j.1539-6924.2008.01190.x.

    Article  Google Scholar 

  • Jonkman, S. N., van Gelder, P. H. A. J. M., & Vrijling, J. K. (2002). Loss of life models for sea and river floods. Flood defence, 1, 196–206.

    Google Scholar 

  • Jonkman, S. N., & Vrijling, J. K. (2008). Loss of life due to floods. Journal of Flood Risk Management, 1(1), 43–56. https://doi.org/10.1111/j.1753-318X.2008.00006.x.

    Article  Google Scholar 

  • Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22.

    Google Scholar 

  • Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Glenview: Scott, Foresman and Company.

    Google Scholar 

  • Maaskant, B., Jonkman, S. N., & Bouwer, L. M. (2009). Future risk of flooding: An analysis of changes in potential loss of life in South Holland (the Netherlands). Environmental Science & Policy, 12(2), 157–169. https://doi.org/10.1016/j.envsci.2008.11.004.

    Article  Google Scholar 

  • MARD (2006). Guideline on natural disaster damage and needs assessment. http://www.ngocentre.org.vn/webfm_send/1533. Accessed 14 Oct 2016.

  • Merz, B., Kreibich, H., & Lall, U. (2013). Multi-variate flood damage assessment: A tree-based data-mining approach. Natural Hazards and Earth System Science, 13(1), 53–64. https://doi.org/10.5194/nhess-13-53-2013.

    Article  Google Scholar 

  • Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article “assessment of economic flood damage”. Natural Hazards and Earth System Science, 10(8), 1697–1724. https://doi.org/10.5194/nhess-10-1697-2010.

    Article  Google Scholar 

  • Mojtahedi, S. M. H., & Oo, B. L. (2016). Coastal buildings and infrastructure flood risk analysis using multi-attribute decision-making. Journal of Flood Risk Management, 9(1), 87–96. https://doi.org/10.1111/jfr3.12120.

    Article  Google Scholar 

  • Nhu, O. L., Thuy, N. T. T., Wilderspin, I., & Coulier, M. (2011). A preliminary analysis of flood and storm disaster data in Vietnam (Global Assessment Report on Disaster Risk Reduction, Vol. 30 Sept 2016): United Nations Development Programme Vietnam.

    Google Scholar 

  • Paul, B. K., & Mahmood, S. (2016). Selected physical parameters as determinants of flood fatalities in Bangladesh, 1972–2013. Natural Hazards. https://doi.org/10.1007/s11069-016-2384-z.

  • R Core Team. (2016). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

    Google Scholar 

  • Sharif, H. O., Jackson, T. L., Hossain, M. M., & Zane, D. (2015). Analysis of flood fatalities in Texas. Natural Hazards Review, 16(1), 04014016. https://doi.org/10.1061/(asce)nh.1527-6996.0000145.

    Article  Google Scholar 

  • Simpson, A., Murnane, R., Saito, K., Phillips, E., Reid, R., & Himmelfarb, A. (2014). Understanding risk in an evolving world: Emerging best practices in natural disaster risk assessment. Washington DC: Global Facility for Disaster Reduction and Recovery, the World Bank.

    Google Scholar 

  • Slobodan, P. S. C. (2012). Floods in a changing climate: Risk management (part of international hydrology series). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(1), 25.

    Article  Google Scholar 

  • Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9, 307. https://doi.org/10.1186/1471-2105-9-307.

    Article  Google Scholar 

  • UN. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (a/RES/70/1). New York: United Nations.

    Google Scholar 

  • UN Country Team in Vietnam. (2016). Vietnam: Situation reports. https://reliefweb.int/organization/unct-viet-nam. Accessed 16 Oct 2017.

  • UNISDR. (2015). Sendai framework for disaster risk reduction 2015–2030. Geneva: United Nations Office for Disaster Risk Reduction (UNISDR).

    Google Scholar 

  • Wang, X., Mahul, O., & Stutley, C. (2010). Weathering the storm: Options for disaster risk financing in Vietnam. Washington, DC: Global Facility for Disaster Reduction and Recovery, the World Bank.

    Google Scholar 

  • Zhai, G., Fukuzono, T., & Ikeda, S. (2006). An empirical model of fatalities and injuries due to floods in Japan. JAWRA Journal of the American Water Resources Association, 42(4), 863–875. https://doi.org/10.1111/j.1752-1688.2006.tb04500.x.

    Article  Google Scholar 

  • Zhou, Q., Leng, G., & Feng, L. (2017). Predictability of state-level flood damage in the conterminous United States: The role of hazard, exposure and vulnerability. Scientific Reports, 7(1), 1–11. https://doi.org/10.1038/s41598-017-05773-4.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chinh Luu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Luu, C., von Meding, J. (2019). Analyzing Flood Fatalities in Vietnam Using Statistical Learning Approach and National Disaster Database. In: Asgary, A. (eds) Resettlement Challenges for Displaced Populations and Refugees. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-319-92498-4_15

Download citation

Publish with us

Policies and ethics