arXiv Machine Learning July 7, 2026 By Karan Ruparell, Tristan Hascoet, Takemasa Miyoshi, Kieran M. R. Hunt, Hannah L. Cloke, Christel Prudhomme, Florian Pappenberger

Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models

Read the original on arXiv Machine Learning →

arXiv:2607. 03217v1 Announce Type: new Abstract: Uncertainty quantification of hydrological predictions is necessary to inform operational decisions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

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arXiv Machine Learning

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arXiv Machine Learning

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Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching

arXiv:2605. 00941v4 Announce Type: replace Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem.

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arXiv AI

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Physics-Informed Machine Learning for Short-Term Flood Prediction

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arXiv Machine Learning

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AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

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