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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Bibliografische gegevens

Hoofdauteurs:	A. A. Ramírez Molina, J. M. Frame, J. Halgren, J. Gong
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Wiley 2025-06-01
Reeks:	Journal of Geophysical Research: Machine Learning and Computation
Onderwerpen:	ungauged basins surrogate gauges synthetic hydrographs LSTM networks deep learning in hydrology hydrologic modeling
Online toegang:	https://doi.org/10.1029/2024JH000405
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