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A tale of two coasts: Unveiling U.S. Gulf and Atlantic coastal cities at high flood risk

Dey, Hemal1; Shao, Wanyun1

Published Apr 06, 2026 on Dryad. https://doi.org/10.5061/dryad.bzkh189q7

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Abstract

Flood impacts in coastal cities can be mitigated through proactive risk management, which requires comprehensive risk assessment. This study develops a data-driven risk assessment framework to identify high-risk coastal cities, estimate exposed populations and their infrastructures, and reveal underlying flood risk factors under dual scenarios—General Flood Damage (GFD) and Extreme Flood Damage (EFD)—along the U.S. Gulf and Atlantic Coasts (USGAC). Using historical flood damage data and 16 factors representing hazard, exposure, and vulnerability, three machine learning models—Support Vector Machine, Random Forest, and Multi-Layer Perceptron—are adopted to assess flood risk. Results indicate that under GFD, 1.14 % of the area with 16.67 % of the populations are at very high risk, while under EFD, 0.53 % of the area with 4.08 % of the population faces very high risk. Eight coastal cities are identified as high-risk. New York City has the largest population at risk (GFD: 4.75 M, EFD: 4.40 M), while New Orleans has the highest relative exposure (~99 % under both). Low elevation is the most influential factor for GFD, and high drainage density is the dominant factor for EFD. This scalable framework offers actionable insights for policymakers to reduce flood risk.