Urban Digital Twin-Based Decision Support for Housing Rebuilding Choices in Catastrophically Flooded Communities

In each year, flooding causes tremendous damages to communities across the United States. A common but difficult decision problem for impacted communities is how to rebuild the housing stocks so that they will be resilient to future flood events. Choices such as elevation and buyout are often intensely debated in local communities, yet communities often lack the adequate data and tools to make wise decisions. In this paper, we present an urban digital twin-based decision support approach to empower local communities to rebuild flood resilient communities. The study shows with an urban digital twin consisting of 3D urban data, high-resolution hydrodynamic models, and regulatory data, stakeholders can effectively reduce the uncertainty in making critical rebuilding decisions such as elevating homes and identifying clusters of improvement opportunities such that the most cost-effective rebuilding choices can be made. Throughout the paper, we will use Manville, New Jersey, a township that was catastrophically impacted by Hurricane Irene, and more recently Hurricane Ida, as a case study to demonstrate the utility of our proposed approach and its superiority over other current approaches.