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This research presents a novel machine-learning algorithm capable of generating plausible extreme weather scenarios without relying on historical data of such events. By learning from standard daily records to exclude implausible outliers, the method enables better risk assessment for critical infrastructure facing unprecedented climate conditions. This approach addresses a fundamental gap in predictive modeling where past data is insufficient to forecast future worst-case outcomes.
Generating scenarios for extreme events, without extreme data
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
Can a city's seawall withstand a blockbuster storm? Will a region's power grid hold up against record-breaking heat? And can a town's fire-fighting resources contain a major wildfire? To answer these questions, communities will first need to know how such extreme events could unfold. How far is a wildfire likely to spread? How much of a region might a storm impact? How long could a heat wave last? But extreme events are notoriously difficult to anticipate. By their nature, they are outliers. In the history of record keeping, extreme events are sporadic and rare. Yet most methods that assess a region's risk depend on extreme events of the past to characterize even more extreme,…
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