RAPID: Collaborative Research: Data Mining and Fusion Between Unmanned Aerial Systems and Social Media Technologies to Improve Emergency Operations
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Disasters such as Hurricane Barry, Hurricane Harvey and others provide an opportunity to document how emergency operation centers (EOCs) can use technology to respond to a natural disaster by understanding and mitigating the uncertainties involved. The use of social media, Unmanned Autonomous Systems/Unmanned Aerial Vehicles (UAVs), real-time disaster modeling, and widespread connectedness means more efficient analysis and flow of information. Immediate information on the location of most damaged areas of a city or stranded people will save lives. Real-time data allows emergency management to develop more targeted response and post disaster recovery plans, and this is regarded as a technological leap from the previous search and rescue strategies attempted decades ago. The proposed research plan formulated by team will not only collect relevant information from real hazard events, but will also analyze and integrate the data to develop the post disaster management frameworks using advanced technologies including UAVs, and various other modes of data collection. Documenting the current operational inefficiencies, technology gaps, and data analysis limitations of EOCs are important for improvements in disaster preparedness and response. Furthermore, this project has time urgency due to the need to collect and utilize time sensitive data from this recent set of storms in the construction of the framework. This RAPID project will leverage Hurricane Barry as a mechanism for creation of a framework that will be integrated into Emergency Operations Centers (EOCs) to support post-disaster analysis and decision-making. Hurricane Barry was accompanied by extensive flooding in coastal Louisiana communities and this has provided a perishable and voluminous data set of social media and UAV imagery for analysis. The project will develop tools for data mining of the social media and fusion with collected UAV imagery for post-disaster analysis. As part of this project, feedback from EOC operators and decision-makers will be provided that will enable enhancement of algorithms and analyses to support recovery as well as response to social media generated rumors. Data from Beaumont EOC from Hurricane Harvey, which also includes data on the recovery of the community will also be collected. By combining data from both regions, a richer dataset will be produced to make comparative analyses and linkages across space, time, disaster level, and socioeconomic factors. This RAPID project will culminate with collecting and archiving (in the National Science Foundation''s Natural Hazards Engineering Research Infrastructure DesignSafe) a rich dataset of EOC operations and technology application from Hurricanes Harvey and Barry. However, it is vital that the knowledge gained be reciprocated back to the EOCs so that they can make improvements for the betterment of US citizens. Research team will develop a manual documenting lessons learned for EOC in Beaumont and Louisiana and how to adopt and implement technology. Technology adoption and implementation requires active learning to retain knowledge. Thus, one-day short courses will be developed that will provide UAV and Twitter demonstrations along with examples of data analysis and fusion. This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.
