用AI优化海岸防洪措施，显著降低飓风损失成本。
Discovering strategies for coastal resilience with AI-based prediction and optimization
- 融合三种AI模型，智能选择防洪设施类型、位置和规模。
- 在佛罗里达州泰尼德尔空军基地预测可节省数十亿美元灾损成本。
- 适合灾害管理、城市规划及公共政策制定者参考。
热带风暴造成广泛财产损失和人员伤亡，是破坏力最强的自然灾害之一。本研究采用人工智能驱动的方法，优化提升沿海地区抗洪韧性的干预方案。通过结合数据驱动的风暴潮生成、干预效果的代理建模以及连续动作强化学习（continuous-armed bandit）求解，系统性优化干预类型、选址与规模，以最小化区域预期洪水损失成本（含建设与维护费用）。方法应用于受飓风迈克尔重创的佛罗里达州泰尼德尔空军基地周边，评估海堤与牡蛎礁组合干预的最优配置。结果表明，该优化策略可比贪心或非最优方案减少数倍损失，潜在节省数十亿美元。研究验证了AI在灾害韧性规划中的高效价值。
原文摘要 · Abstract (English)
Tropical storms cause extensive property damage and loss of life, making them one of the most destructive types of natural hazards. The development of predictive models that identify interventions effective at mitigating storm impacts has considerable potential to reduce these adverse outcomes. In this study, we use an artificial intelligence (AI)-driven approach for optimizing intervention schemes that improve resilience to coastal flooding. We combine three different AI models to optimize the selection of intervention types, sites, and scales in order to minimize the expected cost of flooding damage in a given region, including the cost of installing and maintaining interventions. Our approach combines data-driven generation of storm surge fields, surrogate modeling of intervention impacts, and the solving of a continuous-armed bandit problem. We applied this methodology to optimize the selection of sea wall and oyster reef interventions near Tyndall Air Force Base (AFB) in Florida, an area that was catastrophically impacted by Hurricane Michael. Our analysis predicts that intervention optimization could be used to potentially save billions of dollars in storm damage, far outpacing greedy or non-optimal solutions.
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