Multiscale Learnable Physical Modeling and Data Assimilation Framework: Application to High-Resolution Regionalized Hydrological Simulation of Flash Floods
Ngo Nghi Truyen Huynh,
Pierre-André Garambois,
Benjamin Renard
et al.
Abstract:To advance the discovery of scale-relevant hydrological laws while
better exploiting massive multi-source data, combining machine learning
and process-based modeling is compelling, as recently demonstrated in
lumped hydrological modeling. This article introduces MLPM-PR, a new and
powerful framework standing for Multiscale spatially distributed
Learnable Physical Modeling and learnable Parameter Regionalization with
data assimilation. MLPM-PR crucially builds on a differentiable model
that couples (i) two neur… Show more
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