Multi‐Source Adaptive‐Fusion Transfer Learning for Streamflow Forecasting in Data‐Scarce Catchments

Abstract Streamflow gauging records remain sparse in many regions, limiting hydrological predictions. Transfer Learning (TL), which uses knowledge from data‐rich sources to improve predictions in data‐scarce targets, has emerged as a promising solution. Most TL applications in hydrology pre‐train de...

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Hlavní autoři:	Yuxuan Gao, Dongfang Liang, Edoardo Borgomeo, Hiroshi Cho
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Wiley 2026-06-01
Edice:	Water Resources Research
Témata:	hydrological predictions data‐scarce deep learning transfer learning feature fusion
On-line přístup:	https://doi.org/10.1029/2025WR042495
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