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Resilience Through Big Data: Natural Disaster Vulnerability Context

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Book cover Proceedings of the Fourteenth International Conference on Management Science and Engineering Management (ICMSEM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1190))

Abstract

As a global concern, disaster resilience is considering as the priority sector of almost all countries in the world. This study intends to explore the potential of big data for disaster management through increasing resilience against socio-ecological vulnerability. A qualitative approach focusing desk review and secondary data have been used to substantiate the arguments. This study argues that disaster resilience is an integrated function of the adaptive, absorptive and transformative capacity of an individual or society to withstand and cope with the adverse effects of the disaster. Big data technologies create an opportunity to supply huge information to enhance these capacities so that a social system can face natural disasters properly. This study also emphasizes the major principles of big data for effective use for disaster management like open source tools, strong infrastructure, developing local skills, context-specific data sources, data sharing with ethics, awareness about the right of data and learning from experience. This study also argues that big data is a potential tool for policymakers, administrators, and related stakeholders to take necessary actions during and after disasters like an early warning system, weather forecasting, emergency evacuation, immediate responses, relief distribution, training needs assessment and increasing trained individuals.

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References

  1. Adger, W.N., Hughes, T.P., et al.: Social-ecological resilience to coastal disasters. Science 309(5737), 1036–1039 (2005)

    Article  Google Scholar 

  2. Agrawal, N.: Natural Disasters and Risk Management in Canada. Springer (2018)

    Google Scholar 

  3. Alam, G.M., Alam, K., et al.: Hazards, food insecurity and human displacement in rural riverine Bangladesh: implications for policy. Int. J. Disaster Risk Reduct. 43, 101364 (2019)

    Article  Google Scholar 

  4. Carley, K.M., Malik, M., et al.: Crowd sourcing disaster management: the complex nature of Twitter usage in padang Indonesia. Saf. Sci. 90, 48–61 (2016)

    Article  Google Scholar 

  5. Clark, N., Guiffault, F.: Seeing through the clouds: processes and challenges for sharing geospatial data for disaster management in Haiti. Int. J. Disaster Risk Reduct. 28, 258–270 (2018)

    Article  Google Scholar 

  6. Contreras, D., Forino, G., Blaschke, T.: Measuring the progress of a recovery process after an earthquake: the case of l’aquila, Italy. Int. J. Disaster Risk Reduct. 28, 450–464 (2018)

    Article  Google Scholar 

  7. Di Felice, M., Trotta, A., et al.: Self-organizing aerial mesh networks for emergency communication. In: 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), pp. 1631–1636. IEEE (2014)

    Google Scholar 

  8. Enenkel, M., Saenz, S.M., et al.: Social media data analysis and feedback for advanced disaster risk management arXiv preprint arXiv:180202631 (2018)

  9. Folke, C.: Resilience: the emergence of a perspective for social-ecological systems analyses. Glob. Environ. Change 16(3), 253–267 (2006)

    Article  Google Scholar 

  10. Goldenberg, S.B., Gopalakrishnan, S.G., et al.: The 2012 triply nested, high-resolution operational version of the hurricane weather research and forecasting model (HWRF): track and intensity forecast verifications. Weather Forecast. 30(3), 710–729 (2015)

    Article  Google Scholar 

  11. Horita, F.E., de Albuquerque, J.P., et al.: Bridging the gap between decision-making and emerging big data sources: an application of a model-based framework to disaster management in Brazil. Decis. Support Syst. 97, 12–22 (2017)

    Article  Google Scholar 

  12. Kumar, S.A., Bao, S., et al.: Flooding disaster resilience information framework for smart and connected communities. J. Reliable Intell. Environ. 5(1), 3–15 (2019)

    Article  Google Scholar 

  13. Lu, Z., Cao, G., La Porta, T.: Teamphone: networking smartphones for disaster recovery. IEEE Trans. Mob. Comput. 16(12), 3554–3567 (2017)

    Article  Google Scholar 

  14. Lv, Z., Li, X., Choo, K.K.R.: E-government multimedia big data platform for disaster management. Multimedia Tools Appl. 77(8), 10077–10089 (2018)

    Article  Google Scholar 

  15. Mali, V., Rao, M., Mantha, S.: AHP driven GIS based emergency routing in disaster management. In: International Conference on Advances in Computing, Communication and Control, pp. 237–248. Springer (2013)

    Google Scholar 

  16. Masood, T., So, E., McFarlane, D.: Disaster management operations-big data analytics to resilient supply networks. In: Proceedings of the 24th EurOMA Conference (2017)

    Google Scholar 

  17. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151(4), 264–269 (2009)

    Article  Google Scholar 

  18. Musaev, A., Wang, D., Pu, C.: LITMUS: a multi-service composition system for landslide detection. IEEE Trans. Serv. Comput. 8(5), 715–726 (2014)

    Article  Google Scholar 

  19. Ogie, R.I., Clarke, R.J., et al.: Crowdsourced social media data for disaster management: lessons from the Petajakarta. org project. Comput. Environ. Urban Syst. 73, 108–117 (2019)

    Article  Google Scholar 

  20. Ragini, J.R., Anand, P.R., Bhaskar, V.: Big data analytics for disaster response and recovery through sentiment analysis. Int. J. Inf. Manag. 42, 13–24 (2018)

    Article  Google Scholar 

  21. Sarker, M.N.I., Wu, M., et al.: Livelihood vulnerability of riverine-Island dwellers in the face of natural disasters in Bangladesh. Sustainability 11(6), 1623 (2019)

    Article  Google Scholar 

  22. Tomaszewski, B., Judex, M., et al.: Geographic information systems for disaster response: a review. J. Homel. Secur. Emerg. Manag. 12(3), 571–602 (2015)

    Google Scholar 

  23. Vandenbroucke, J.P., Von Elm, E., et al.: Strengthening the reporting of observational studies in epidemiology (strobe): explanation and elaboration. Ann. Intern. Med. 147(8), W–163 (2007)

    Article  Google Scholar 

  24. Yang, C., Su, G., Chen, J.: Using big data to enhance crisis response and disaster resilience for a smart city. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp 504–507. IEEE (2017)

    Google Scholar 

  25. Yu, M., Yang, C., Li, Y.: Big data in natural disaster management: a review. Geosciences 8(5), 165 (2018)

    Article  Google Scholar 

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Acknowledgements

This article is funded by Sichuan University Innovation Spark Project (No.2018hhs-21), Sichuan University Central University Basic Scientific Research Project (No.skqx201501).

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Correspondence to Min Wu .

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Sarker, M.N.I., Wu, M., Chanthamith, B., Ma, C. (2020). Resilience Through Big Data: Natural Disaster Vulnerability Context. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_8

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