NSF Convergence Accelerator: Symposium on Big Data and AI-Driven Disaster Management for Planning, Response, Recovery, and Resiliency

University Of South Carolina At Columbia, Columbia SC

Investigators

Abstract

This project supports efforts to organize and host a workshop to expand collaboration between stakeholders wishing to more effectively use big data and artificial intelligence tools to more effectively prepare for, respond to, and recover from natural disasters. The United States experienced more than sixteen natural hazards causing greater than a billion dollars of impact each in 2017 alone, and many predictions point to more frequent and more severe disasters. Because of this organizational and research efforts are directed toward improving planning, immediate response, and resiliency against natural disasters. However, those efforts are often siloed based on the type of disaster (e.g. wildfires vs. landslides). An additional challenge is understanding and using the explosion of technological capabilities that provide so many new data sources, including physical (e.g., sensors, internet-of-things tools, drones), cyber (e.g., hazard databases, open government data), and social (e.g. microblog streams) data of various modalities (text/images/videos). This workshop will identify areas of future research and inter-organizational collaboration that are needed to transform big data, artificial intelligence, and machine learning tools into actionable knowledge for all types of disaster management. This workshop will bring together 30-40 stakeholders including practitioners (e.g. first responders, local/state/federal government), academia (including fields of computer, social, physical sciences. and engineering), and industry (for-profit, non-profit) to discuss the information and data technology needs for all phases of disaster resilience, from planning to response to recovery. The workshop will identify major research challenges / opportunities and the potential for the research areas to transition to practical use in the short term. These insights will help describe the potential for this area to serve as a future topic for the NSF Convergence Accelerator. Big data harnessed with artificial intelligence (AI) tools are needed to improve productivity, efficiency, and decision making in disaster preparedness and response, but big data and AI are also relevant to many other challenges our society is facing. Therefore, this workshop should also provide an example of how disparate data and tools may be integrated for planning and decision support. This effort builds upon investments made by other programs at NSF, including Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP), Interdisciplinary Research in Hazards and Disasters (Hazard-SEES), and Smart & Connected Communities (S&CC), as well as investments by many other U.S. federal agencies. 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.