Tropical storms cause extensive property damage and loss of life, making them one of the most destructive types of natural hazards. The development of predictive models that identify interventions effective at mitigating storm impacts has considerable potential to reduce these adverse outcomes. In this study, we use an artificial intelligence (AI)-driven approach for optimizing intervention schemes that improve resilience to coastal flooding. We combine three different AI models to optimize the selection of intervention types, sites, and scales in order to minimize the expected cost of flooding damage in a given region, including the cost of installing and maintaining interventions. Our approach combines data-driven generation of storm surge fields, surrogate modeling of intervention impacts, and the solving of a continuous-armed bandit problem. We applied this methodology to optimize the selection of sea wall and oyster reef interventions near Tyndall Air Force Base (AFB) in Florida, an area that was catastrophically impacted by Hurricane Michael. Our analysis predicts that intervention optimization could be used to potentially save billions of dollars in storm damage, far outpacing greedy or non-optimal solutions.

This compilation highlights the power of the storm and what residents face in the weeks ahead. Days after powerful Hurricane Michael made landfall Wednesday just north of Mexico Beach, Florida, thousands of people have been reported missing to local authorities. Emergency crews made it their mission Friday to search for people after the Category 4 storm barreled into the Florida Panhandle before making its way to southwest Georgia and South Carolina – while also lashing North Carolina and Virginia. At least 17 people have been killed. Emergency officials said they received thousands of calls asking about missing people, but with cellphone service out across a wide area, they found it impossible to know who among those unaccounted for was safe but just unable to dial out to friends or family.