Advancing spatial hurricane storm surge risk assessment using machine learning and simulations under changing climate scenarios

Hurricane risk assessments under climate change are essential for resilient infrastructure planning, yet they remain constrained by limited and inconsistent historical damage records. This study develops a spatial framework for assessing storm surge-induced hurricane building damage under future climate scenarios. Ten thousand years of synthetic hurricane tracks and storm-surge simulations were generated and overlaid with building-level insurance data. A machine learning model trained on historical insurance losses was then applied to the simulated hurricane dataset to estimate building-level damage under RCP-4.5 and RCP-8.5 scenarios. The results indicate a spatial shift in projected hurricane damage from the western Florida coastline toward the eastern Atlantic coast. Although hurricane frequency is projected to decrease, storm intensity increases substantially, with Category 5 hurricanes rising by 58% under RCP-4.5 and 104% under RCP-8.5. Miami emerges as the highest-risk location. The proposed framework integrates synthetic hazard simulation and machine learning to support climate-resilient spatial risk assessment.