PROVIDENCE, R.I. [Brown University] — When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics or “rogue waves” that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there’s just not enough data on them to use predictive models to accurately forecast when they’ll happen next.
But a team of researchers from Brown University and Massachusetts Institute of Technology say it doesn’t have to be that way.
In a new study in Nature Computational Science, the scientists describe how they combined statistical algorithms — which need less data to make accurate, efficient predictions — with a powerful machine learning technique developed at Brown and trained it to predict scenarios, probabilities and sometimes even the timeline of rare events despite the lack of historical record on them.
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