Sundial model predicts floods in data-scarce regions
Researchers from UT Austin and Hydrotify LLC have demonstrated that Sundial, a time-series foundation model trained on a trillion data points, can accurately forecast river flows without local records. Using a decoder-only architecture and "TimeFlow Loss," the model matches traditional LSTM performance in zero-shot testing, providing a breakthrough for flood warnings in underserved regions.
Sundial marks a significant shift from localized hydrological modeling to "foundation-first" water management, potentially democratizing flood safety for the Global South. Its zero-shot performance matches trained LSTMs without requiring local data, a major milestone for disaster response in unmonitored regions. By utilizing flow-matching and predictive distributions, the model provides a range of plausible future scenarios that improve risk assessment compared to traditional deterministic outputs.
DISCOVERED
67d ago
2026-03-21
PUBLISHED
67d ago
2026-03-21
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Secure-Technology-78