用深度学习模拟美国沿海风暴潮风险，预测百年一遇潮位与人口受威胁情况。
Projecting U.S. coastal storm surge risks and impacts with deep learning
- 基于90万次合成飓风事件，用深度学习高效估算沿海风暴潮风险。
- 到本世纪末，飓风强度与海平面上升使受威胁人口增加50%。
- 佛罗里达风险显著上升，佐治亚和南卡罗来纳有关键临界点。
风暴潮是热带气旋（TC）带来的最致命灾害之一，但因其罕见性和物理复杂性，当前及未来风险评估困难。我们利用深度学习风暴潮模型，基于90万次合成热带气旋事件，评估美国沿海风暴潮风险，并考虑未来飓风行为变化与海平面上升的影响。推导出的历史100年一遇潮位（年超概率1%）与历史观测及其他建模方法吻合良好。结合淹没模型发现，到本世纪末，飓风强度增强与海平面上升将导致受威胁人口增加50%。关键发现包括佛罗里达州风险显著升高，以及佐治亚州和南卡罗来纳州的临界阈值。
原文摘要 · Abstract (English)
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.
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