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Resilience Through Big Data: Natural Disaster Vulnerability Context

Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

  • Disaster resilience
  • Administrative resilience
  • Community resilience
  • Disaster management
  • Environment

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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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