3 papers
cs.LG2026
Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models
Karan Ruparell, Tristan Hascoet, Takemasa Miyoshi +4
Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have advanced probabilistic streamfl…
cs.LG2026
AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
Maria Luisa Taccari, Kenza Tazi, Oisín M. Morrison +7
Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transition…
cs.LG2024
Hydra-LSTM: A semi-shared Machine Learning architecture for prediction across Watersheds
Karan Ruparell, Robert J. Marks, Andy Wood +5
Long Short Term Memory networks (LSTMs) are used to build single models that predict river discharge across many catchments. These models offer greater accuracy than models trained…