Rapid Damage Assessment Following Natural Disasters through Information Integration
Publication: Natural Hazards Review
Volume 22, Issue 4
Abstract
A rapid damage assessment is essential for practitioners to make timely and informed decisions following a disaster. This research aims to provide such an assessment through integrating multisource information that comprises hazard characteristic, community exposure, community vulnerability, and social media information. To illustrate the reliability of the proposed strategy, supervised learning was employed because its performance highly relies on the quality of information integration. In detail, reference samples were prepared using the information of three recent hurricanes: Harvey, Irma, and Michael. Then two supervised learning models—multiple linear regression and support vector regression—were trained using the reference samples from Hurricanes Harvey and Irma. The trained models were tested using the reference samples from Hurricane Michael to demonstrate the applicability of the proposed approach. Theoretically, this research proves the concept of integrating multisource information for achieving a rapid damage assessment. Practically, this research proposes the whole pipeline from information collection to final prediction for deriving a rapid damage assessment following disasters.
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Data Availability Statement
All data, models, or codes generated or used during the study are available from the corresponding author by request.
Acknowledgments
This work was supported by the Thomas F. and Kate Miller Jeffress Memorial Trust (Grant No. 223476). Any opinions, findings, and conclusions, or recommendations expressed in this article are those of the authors and do not reflect the views of the Thomas F. and Kate Miller Jeffress Memorial Trust.
References
Alma, Ö. G. 2011. “Comparison of robust regression methods in linear regression.” Int. J. Contemp. Math. Sci. 6 (9): 409–421.
Beleites, C., R. Baumgartner, C. Bowman, R. Somorjai, G. Steiner, R. Salzer, and M. G. Sowa. 2005. “Variance reduction in estimating classification error using sparse datasets.” Chemom. Intell. Lab. Syst. 79 (4): 91–100. https://doi.org/10.1016/j.chemolab.2005.04.008.
Berkmann, J. 2006. “Measuring vulnerability to promote disaster-resilient societies: Conceptual frameworks and definitions.” In Measuring vulnerability to natural hazards: Towards disaster resilient societies, 9–54. Tokyo: United Nations University Press.
Birkmann, J., and T. Welle. 2015. “Assessing the risk of loss and damage: Exposure, vulnerability and risk to climate-related hazards for different country classifications.” Int. J. Global Warming 8 (2): 191–212. https://doi.org/10.1504/IJGW.2015.071963.
Bishop, C. M. 2006. Pattern recognition and machine learning. Berlin: Springer.
Bramer, M. 2007. Principles of data mining. Berlin: Springer.
Bureau of Economic Analysis. 2020. “Gross domestic product.” Accessed September 20, 2020. https://www.bea.gov/data/gdp/gross-domestic-product.
Cervone, G., E. Sava, Q. Huang, E. Schnebele, J. Harrison, and N. Waters. 2016. “Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study.” Int. J. Remote Sens. 37 (1): 100–124. https://doi.org/10.1080/01431161.2015.1117684.
Chen, Y., Q. Wang, and W. Ji. 2020a. “Assessing disaster impacts on highways using social media: Case study of Hurricane Harvey.” In Proc., Construction Research Congress, 562–571. Reston, VA: ASCE.
Chen, Y., Q. Wang, and W. Ji. 2020b. “Rapid assessment of disaster impacts on highways using social media.” J. Manage. Eng. 36 (5): 04020068. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000836.
Cook, L. 2019. “What weather conditions create a hurricane.” Accessed September 20, 2020. https://sciencing.com/weather-conditions-create-hurricane-5152715.html.
Coronese, M., F. Lamperti, K. Keller, F. Chiaromonte, and A. Roventini. 2019. “Evidence for sharp increase in the economic damages of extreme natural disasters.” Proc. Natl. Acad. Sci. 116 (43): 21450–21455. https://doi.org/10.1073/pnas.1907826116.
Dargin, J. S., C. Fan, and A. Mostafavi. 2021. “Vulnerable populations and social media use in disasters: Uncovering the digital divide in three major US hurricanes.” Int. J. Disaster Risk Reduct. 54 (Dec): 102043. https://doi.org/10.1016/j.ijdrr.2021.102043.
Deryugina, T. 2017. “The fiscal cost of hurricanes: Disaster aid versus social insurance.” Am. Econ. J. Econ. Policy 9 (3): 168–198. https://doi.org/10.1257/pol.20140296.
Donner, W., and H. Rodriguez. 2008. “Population composition, migration and inequality: The influence of demographic changes on disaster risk and vulnerability.” Soc. Forces 87 (2): 1089–1114. https://doi.org/10.1353/sof.0.0141.
Draper, N. R., and H. Smith. 1998. Applied regression analysis. New York: Wiley.
Duggan, M., and J. Brenner. 2013. The demographics of social media users. Washington, DC: Pew Research Center.
Elser, S., R. Upton, and C. Gann. 2020. “Small area income and poverty estimates: 2018.” Accessed September 20, 2020. https://www.census.gov/library/publications/2020/demo/p30-06.html.
Fan, C., M. Esparza, J. Dargin, F. Wu, B. Oztekin, and A. Mostafavi. 2020. “Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters.” Comput. Environ. Urban Syst. 83 (May): 101514. https://doi.org/10.1016/j.compenvurbsys.2020.101514.
FEMA. 2020a. “Assistance for governments and private non-profits after a disaster.” Accessed September 20, 2020. http://www.fema.gov/public-assistance-local-state-tribal-and-non-profit.
FEMA. 2020b. “Damage assessment operations manual—A guide to assessing damage and impact.” Accessed September 20, 2020. https://www.fema.gov/sites/default/files/2020-07/Damage_Assessment_Manual_April62016.pdf.
FEMA. 2020c. “Individual assistance program and policy guide.” Accessed September 20, 2020. https://www.fema.gov/individual-assistance-program-and-policy-guide.
Fernandez Galarreta, J., N. Kerle, and M. Gerke. 2015. “UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning.” Nat. Hazards Earth Syst. Sci. 15 (6): 1087–1101. https://doi.org/10.5194/nhess-15-1087-2015.
Frank, T. 2021. “Advisers rebuke FEMA for racial disparities in disaster aid.” Scientific American, January 7, 2021.
Imran, M., C. Castillo, F. Diaz, and S. Vieweg. 2018. “Processing social media messages in mass emergency: Survey summary.” In Proc., Int. World Wide Web Conf. Communication, 507–511. New York: Association for Computing Machinery.
Kakooei, M., and Y. Baleghi. 2017. “Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment.” Int. J. Remote Sens. 38 (8–10): 2511–2534. https://doi.org/10.1080/01431161.2017.1294780.
Kryvasheyeu, Y., H. Chen, N. Obradovich, E. Moro, P. Van Hentenryck, J. Fowler, and M. Cebrian. 2016. “Rapid assessment of disaster damage using social media activity.” Sci. Adv. 2 (Sep): e1500779. https://doi.org/10.1126/sciadv.1500779.
NAC (National Association of Counties). 2019. “Emergency management in county government: A national survey.” Accessed September 20, 2020. https://www.naco.org/resources/emergency-management-county-government-national-survey.
NOAA (National Oceanic and Atmospheric Administration). 1971. “Saffir-Simpson hurricane wind scale.” Accessed September 20, 2020. https://www.nhc.noaa.gov/aboutsshws.php.
NOAA (National Oceanic and Atmospheric Administration). 2020a. “Hurricane and tropical storm watches, warnings, advisories and outlooks.” Accessed September 20, 2020. https://www.weather.gov/safety/hurricane-ww.
NOAA (National Oceanic and Atmospheric Administration). 2020b. “National Weather Service.” Accessed September 20, 2020. https://w2.weather.gov/climate/.
Pedregosa, F., G. Varoquaux, A. Gramfort, and V. Michel. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Nov): 285–2830. https://doi.org/10.4018/978-1-5225-9902-9.ch008.
Peduzzi, P., H. Dao, C. Herold, and F. Mouton. 2009. “Assessing global exposure and vulnerability towards natural hazards: The disaster risk index.” Nat. Hazards Earth Syst. Sci. 9 (4): 1149–1159. https://doi.org/10.5194/nhess-9-1149-2009.
Santos, R. 2019. “Fairness, not just accuracy, is vital to the 2020 census.” Accessed September 20, 2020. https://www.urban.org/urban-wire/fairness-not-just-accuracy-vital-2020-census.
Sawada, Y., and Y. Takasaki. 2017. “Natural disaster, poverty, and development: An introduction.” World Dev. 94: 2–15. https://doi.org/10.1016/j.worlddev.2016.12.035.
Semega, J., M. Kollar, J. Creamer, and A. Mohanty. 2020. “Income and poverty in the United States: 2018.” Accessed September 20, 2020. https://www.census.gov/library/publications/2019/demo/p60-266.html.
Sturgis, S. 2018. “Recent disasters reveal racial discrimination in FEMA aid process.” Facing South, September 24, 2018.
Suykens, J. A. K., and J. Vandewalle. 1999. “Least squares support vector machine classifiers.” Neural Process. Lett. 9 (Oct): 293–300. https://doi.org/10.1023/A:1018628609742.
UNISDR (United Nations Office for Disaster Risk Reduction). 2015. “Global assessment report on disaster risk reduction.” Accessed September 20, 2020. https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2019.
US Census Bureau. 2020. “Center of population.” Accessed September 20, 2020. https://data.census.gov/cedsci/table?q=United%20States&g=0100000US&tid=ACSDP1Y2019.DP05&hidePreview=true.
Wang, W., J. J. Qu, X. Hao, Y. Liu, and J. A. Stanturf. 2010. “Post-hurricane forest damage assessment using satellite remote sensing.” Agric. For. Meteorol. 150 (1): 122–132. https://doi.org/10.1016/j.agrformet.2009.09.009.
Willmott, C. J., and K. Matsuura. 2005. “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance.” Clim. Res. 30 (2): 79–82. https://doi.org/10.3354/cr030079.
Winsemius, H. C., B. Jongman, T. I. E. Veldkamp, S. Hallegatte, M. Bangalore, and P. J. Ward. 2018. “Disaster risk, climate change, and poverty: Assessing the global exposure of poor people to floods and droughts.” Environ. Dev. Econ. 23 (3): 328–348. https://doi.org/10.1017/S1355770X17000444.
Yuan, F., and R. Liu. 2018. “Feasibility study of using crowdsourcing to identify critical affected areas for rapid damage assessment: Hurricane Matthew case study.” Int. J. Disaster Risk Reduct. 28 (Jun): 758–767. https://doi.org/10.1016/j.ijdrr.2018.02.003.
Yuan, F., and R. Liu. 2020. “Mining social media data for rapid damage assessment during Hurricane Matthew: Feasibility study.” J. Comput. Civ. Eng. 34 (3): 05020001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000877.
Zien, A., N. Krämer, S. Sonnenburg, and G. Rätsch. 2009. The feature importance ranking measure, 694–709. Berlin: Springer.
Zorn, M. 2018. Natural disasters and less developed countries, 59–78. Berlin: Springer.
Zou, H., and T. Hastie. 2005. “Regularization and variable selection via the elastic net.” J. R. Stat. Soc. Ser. B 67 (2): 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
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Published In
Natural Hazards Review
Volume 22 • Issue 4 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 28, 2020
Accepted: Apr 29, 2021
Published online: Aug 5, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 5, 2022
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