| Autor: |
Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Colloque SHF 2023 - Pr{\'e}vision des crues et des inondations, Nov 2023, Toulouse, France |
| Druh dokumentu: |
Working Paper |
| Popis: |
Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. The transfer functions rely on: (i) a multilayer perceptron enabling a seamless flow of gradient computation to employ machine learning optimization algorithms, or (ii) a multivariate regression mapping optimized by variational data assimilation algorithms and guided by Bayesian estimation, addressing the equifinality issue of feasible solutions. The approach involves incorporating the inferable regionalization mappings into a differentiable hydrological model and optimizing a cost function computed on multi-gauge data with accurate adjoint-based spatially distributed gradients. |
| Databáze: |
arXiv |
| Externí odkaz: |
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