# Natural disasters

Published articles for Natural disasters.

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## Generating scenarios for extreme events, without extreme data

DevFeed: [Generating scenarios for extreme events, without extreme data](<https://devfeed.tech/articles/generating-scenarios-for-extreme-events-without-extreme-data-37953.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/generating-scenarios-extreme-events-without-extreme-data-0824>)

Author: Jennifer Chu | MIT News

Published: 2026-08-24T18:00:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Critical Infrastructure](<https://devfeed.tech/topics/critical-infrastructure.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [center-for-computational-science-and-engineering](<https://devfeed.tech/tags/center-for-computational-science-and-engineering.md>), [climate](<https://devfeed.tech/tags/climate.md>), [climate-risk-assessment](<https://devfeed.tech/tags/climate-risk-assessment.md>), [computer-modeling](<https://devfeed.tech/tags/computer-modeling.md>), [critical-infrastructure](<https://devfeed.tech/tags/critical-infrastructure.md>), [data](<https://devfeed.tech/tags/data.md>), [extreme-event-aware](<https://devfeed.tech/tags/extreme-event-aware.md>), [extreme-weather](<https://devfeed.tech/tags/extreme-weather.md>), [fire](<https://devfeed.tech/tags/fire.md>), [heat](<https://devfeed.tech/tags/heat.md>), [idss](<https://devfeed.tech/tags/idss.md>), [kai-chang](<https://devfeed.tech/tags/kai-chang.md>), [learning-fefb62e9fa83](<https://devfeed.tech/tags/learning-fefb62e9fa83.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mechanical-engineering](<https://devfeed.tech/tags/mechanical-engineering.md>), [mit-meche](<https://devfeed.tech/tags/mit-meche.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [natural-disasters](<https://devfeed.tech/tags/natural-disasters.md>), [research](<https://devfeed.tech/tags/research.md>), [risk](<https://devfeed.tech/tags/risk.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [storm](<https://devfeed.tech/tags/storm.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [themis-sapsis](<https://devfeed.tech/tags/themis-sapsis.md>), [weather](<https://devfeed.tech/tags/weather.md>), [weather-prediction](<https://devfeed.tech/tags/weather-prediction.md>)

### AI overview

MIT engineers developed a machine-learning algorithm that generates plausible future extreme-event scenarios without requiring past extreme events in the training data. It learns from available records, filters out implausible weather scenarios, and estimates events' frequency, size, intensity, duration, and area of impact to help planners prepare.

### Source excerpt

A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.