Multi-step ahead streamflow and uncertainty forecasting using a HyMoLAP rainfall-runoff model-based framework integrated with Bayesian neural networks in the Ouémé river basin, Benin.
Multi-step forecasting is crucial for capturing future streamflow variations and managing water resources but remains challenging due to limited accuracy of upstream flow forecasts and meteorological predictions over lead times. While data-driven methods are commonly used, this study extends the Hyd...
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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Public Library of Science (PLoS)
2025-01-01
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| Serie: | PLoS ONE |
| Accesso online: | https://doi.org/10.1371/journal.pone.0333590 |
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