A Proof of Concept for Improving Estimates of Ungauged Basin Streamflow via an LSTM‐Based Synthetic Network Simulation Approach

Abstract This study introduces a machine learning approach to address the critical challenge of limited real‐time flow data in river basins, particularly for calibrating large‐scale hydrologic models. These models often rely on uncertain parameter transfers from gauged to ungauged regions, hindering...

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Auteurs principaux:	A. A. Ramírez Molina, J. M. Frame, J. Halgren, J. Gong
Format:	Artigo
Langue:	Inglês
Publié:	Wiley 2025-06-01
Collection:	Journal of Geophysical Research: Machine Learning and Computation
Sujets:	ungauged basins surrogate gauges synthetic hydrographs LSTM networks deep learning in hydrology hydrologic modeling
Accès en ligne:	https://doi.org/10.1029/2024JH000405
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