Discovering strategies for coastal resilience with AI-based prediction and optimization
This addresses the problem of reducing property damage and loss of life from tropical storms for coastal communities, representing an incremental improvement through optimization of existing intervention types.
The study tackled the problem of mitigating coastal flooding damage from tropical storms by developing an AI-driven approach to optimize intervention schemes, predicting potential savings of billions of dollars in storm damage compared to non-optimal solutions.
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.