This study presents a validated framework to quantify regional sea-level risk on U.S. coasts by (i) extracting trends and seasonality from satellite altimetry (ADT, GMSL), (ii) learning regional dynamical regimes via PCA-embedded KMeans on gridded ADT time series, and (iii) coupling these regimes with socioeconomic exposure (population, income, ocean-sector employment/GDP) and wetland submersion scoring. Relative to linear and ARIMA/SARIMA baselines, a sinusoid+trend fit and an LSTM forecaster reduce out-of-sample error (MAE/RMSE) across the North Atlantic, North Pacific, and Gulf of Mexico. The clustering separates high-variability coastal segments, and an interpretable submersion score integrates elevation quantiles and land cover to produce ranked adaptation priorities. Overall, the framework converts heterogeneous physical signals into decision-ready coastal risk tiers to support targeted defenses, zoning, and conservation planning.

This paper addresses the critical issue of sea level rise due to climate change through comprehensive analysis and projections. Our approach integrates satellite data, climate models, and socioeconomic factors to assess vulnerabilities and risks to coastal areas. Key findings include significant global temperature increases leading to accelerated glacier melting and rising sea levels, disproportionately impacting the U.S. coastline. Major coastal cities in the U.S. face increased risks of flooding, storm surges, and erosion, resulting in potential widespread displacement, property loss, and adverse health outcomes. The economic consequences are substantial, with key sectors such as tourism, real estate, and fisheries facing direct threats. We provide evidence-based recommendations for targeted mitigation and adaptation strategies to address these challenges.