Evacuation and public sheltering move people from harm’s way and are common life-saving strategies in response to severe weather such as flooding and hurricanes. However, some citizens may exhibit lower propensities to evacuate and seek public shelter due to transportation challenges, past experiences, risk perceptions, and concerns about the availability of critical services at shelters. From the planning perspective of emergency management, choosing which shelters to open and when based on risks to infrastructure, optimizing resource allocation in operating public shelters, and estimating shelter demand present challenges. Current decision support systems rely primarily on weather forecasts, flood risk assessments, retrospective knowledge of shelter usage, and past public behavior. However, such data inputs are unable to fully account for the dynamic nature of evolving needs and movement behavior of the public, as well as failure risks of infrastructure necessary to run shelter operations due to their uncertain and dynamic interdependencies like transportation and power. This research will fill this gap in current decision support systems to perform continual risk analysis for shelter planning to facilitate optimal decision-making under rapidly evolving events. This project advances the well-being of citizens by reducing risk and helping communities increase resilience to severe emergency events. This project proposes to design and test an Artificial Intelligence (AI)