SBIR Phase I: CAS: DIGITAL TWIN FOR CLIMATE RESILIENCE ANALYTICS

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This Small Business Innovation Research (SBIR) Phase I project augments community resilience to climate hazards by improving the situational awareness of public organizations, officials, and emergency managers. The project is focused on harnessing the data revolution in dealing with climate hazards. The team develops a digital twin technology for disaster preparedness, response, and recovery. Climate hazards (hurricanes and floods, in particular) are the most prominent stressors for communities in the United States and worldwide, causing dire physical, social, and economic hardships. The outcomes of this research have the potential for significant societal benefits that could enhance the public safety of millions of U.S. residents exposed to climate hazards and potentially lead to millions of dollars in avoided disaster management costs through proactive preparedness. The project could transform the ability of decision-makers, emergency managers, and responders to tailor their strategies and technologies to enhance situational awareness in dealing with climate hazards. This Small Business Innovation Research (SBIR) Phase I project delves into the intricate challenges of creating and designing a state-of-the-art digital twin technology that harnesses the power of community-scale big data and machine intelligence, offering a proactive and predictive lens on community preparedness, evacuation measures, protective actions, and post-emergency event recovery. The research activities include: (1) creating and testing computational methods, algorithms and metrics for specifying the extent of a populations' preparedness, evacuation planning, and recovery at the block group scale in near-time; (2) prototyping and optimizing the architecture of a web-based digital twin platform with effective data fusion and computation workflows in order to implement the created methods and algorithms and visualize the output insights in an intuitive, timely, and decision-friendly manner; (3) evaluating the performance of the aforementioned computational methods embedded in the digital twin technology prototype in the context of recent climate hazard events; and (4) demonstrating the use case of the digital twin prototype for emergency response and management applications through existing and growing partnerships.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.