The Potential of Data Analytics in Disaster Management
In the era of social media, big data and Industry 4.0, technology has to make more contributions to help nimble decision making in response to severe disasters, both natural (including climate-related extreme events) and manmade, by providing the right solutions. Different reports and experiences originating from recent disasters and their crisis management processes have highlighted the need for a resilient and innovative disaster decision support system, even at the modern, developed and well-equipped communities working upon real-time big data. The aim of this paper is to propose a tool to foster preparedness, response, recovery, and mitigation as the fundamental steps of catastrophe management via innovation for disaster-resilient societies. The proposed tool consists of a novel conceptual hybridization of virtual experiments, machine learning, block chain, and database management technologies to overcome limitations of currently used technologies. This tool will utilize innovation in information registration and distribution, data exploration and discovery for generating reliable solutions. The most impressive implications of the proposed technology are its ability to measure community sentiments, generate smartly-designed hazard scenarios and propose the best emergency evacuation plot on 3D notifications as an innovative distinct feature.
KeywordsSocial media Data mining Optimization Blockchain
- Bañgate, J., Dugdale, J., Adam, C., & Beck, E. (2017). A review on the influence of social attachment on human mobility during crisis. In T. Comes, F. Benaben, C. Hanachi, & M. Lauras (Eds.). Proceedings of the 14th ISCRAM Conference.Google Scholar
- Bañgate, J., Dugdale, J., Beck, E., & Adam, C. (2018, February). SOLACE a multi-agent model of human behaviour driven by social attachment during seismic crisis. https://doi.org/10.1109/ict-dm.2017.8275676.
- Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., Kasperson, J. X., & Ratick, S. (1988). The social amlplification of risk: A conceptual framework. Risk Analysis, 8(2), 177–187. https://doi.org/10.1111/j.1539-6924.1988.tb01168.x.CrossRefGoogle Scholar
- Onorati, T., Díaz, P., & Carrion, B. (2018). From social networks to emergency operation centers: A semantic visualization approach. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.01.052.
- Rossi, C., Acerbo, F. S., Ylinen, K., Juga, I., Nurmi, P., & Bosca, A. (2018, February). Early detection and information extraction for weather-induced floods using social media streams. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2018.03.002. Elsevier Ltd: 0–1.CrossRefGoogle Scholar
- Wukich, C., & Mergel, I. (2015). Closing the citizen-government communication gap: Content, audience, and network analysis of government tweets. Journal of Homeland Security and Emergency Management, 12(3), 707–735. https://doi.org/10.1515/jhsem-2014-0074.