Can AI help coastal cities prepare for rising seas and extreme events?

2026-06-09 · Source: Artificial intelligence (AI) – The Conversation · Field: Science & Research — Environmental Science & Earth Systems, Artificial Intelligence & Machine Learning · Depth: Advanced, short

Summary

A novel artificial intelligence model has been developed to predict extreme storm surges with high accuracy, even under future climate conditions. This AI model runs significantly faster than traditional physics-based models, enabling researchers and practitioners to better assess coastal flood risk for adaptation planning. Extreme coastal events, like storm surges, pose a growing threat to the more than 10% of the global population living in low-lying coastal regions, exacerbated by rising sea levels and increasing event intensity. Projecting these events is challenging due to complex, nonlinear interactions and unquantified uncertainties. The new AI emulator, detailed in a study published in "Earth's Future", learns to reproduce the outputs of computationally expensive physics-based simulations, demonstrating success in the New York City coastal area, which experienced over \$60 billion in damage from Hurricane Sandy in 2012. This combined approach offers a tool for rapid scenario generation and uncertainty characterization, though further testing across wider climate scenarios and integration into operational frameworks are next steps.

Key takeaway

For coastal planners and policymakers assessing future flood risks, this AI model offers a critical tool to overcome computational limitations of traditional physics-based simulations. You can now explore large ensembles of future extreme storm surge scenarios much faster, enabling more robust infrastructure design and disaster preparedness. Integrate these AI emulators into your risk assessment frameworks to better characterize uncertainties and inform adaptation strategies for a changing climate.

Key insights

A fast AI emulator accurately predicts extreme storm surges by learning from physics-based models, enhancing coastal flood risk assessment.

Principles

• AI and physics-based models are complementary.
• AI can reliably project rare, high-impact events.
• Rapid scenario generation improves uncertainty characterization.

Method

An AI emulator is trained on openly available physics-based simulations (e.g., GTSM) to reproduce complex storm surge dynamics and extreme events, then tested for reliability under future climate scenarios.

In practice

• Use AI emulators for rapid coastal flood risk assessment.
• Integrate AI tools into operational risk frameworks.
• Test AI robustness across diverse climate scenarios.

Topics

• AI Models
• Storm Surge Prediction
• Coastal Flood Risk
• Climate Adaptation
• Physics-based Modeling
• New York City

Best for: AI Scientist, Research Scientist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial intelligence (AI) – The Conversation.