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arXiv Machine Learning By Justin Gallant, Tyler Wilson

Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes

Read the original on arXiv Machine Learning →

The study examines how ensemble spread from Long Short-Term Memory (LSTM) models, used for streamflow forecasting, behaves under increasingly extreme flow conditions due to climate change. It finds that prediction error grows relative to the corrected spread—up to four times larger at the highest flows—and that under-dispersion is most pronounced for extreme flows beyond the training record, reaching an 18‑fold discrepancy. While the spread still ranks errors well (near‑oracle ranking), it systematically understates risk, especially for the most critical extreme events.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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