Projecting U.S. coastal storm surge risks and impacts with deep learning
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Abstract (arXiv)
Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100-year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 90 references that are in the map. First posted on arXiv 16 Jun 2025.
Builds on
6 of 90 references.
References · 6
- 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
- 2015An Introduction to Convolutional Neural Networkscited by 514
- 2023Neural General Circulation Models for Weather and Climatecited by 239
- 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 177
- 2022An advanced spatio-temporal convolutional recurrent neural network for storm surge predictionscited by 7
- 2024Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracycited by 6
Built on by
Citing papers per year: 2026: 1.
1 paper.