Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins

Hydrological models have made significant advances in methodologies and applications in recent years. However, there is still a need to address the challenge of modeling in areas with limited or no data. This study proposes a transferred Long Short-Term Memory (T-LSTM) network based on transfer lear...

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Vydané v:	Water resources management Ročník 39; číslo 9; s. 4493 - 4507
Hlavní autori:	Xie, Zaichao, Xu, Wei, Zhu, Bing, Yin, Shiming, Yang, Yi, Li, Xiaojie, Wang, Sufan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.07.2025 Springer Nature B.V
Predmet:	Algorithms Artificial intelligence Atmospheric Sciences Basins China Civil Engineering Earth and Environmental Science Earth Sciences Environment Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrologic data Hydrologic models Hydrology Hydrology/Water Resources Hydrometeorology Interbasin transfers K-nearest neighbors algorithm Long short-term memory Machine learning Networks Neural networks Performance evaluation Precipitation River basins River flow River forecasting Rivers Soil and Water Assessment Tool model Stream flow Time series Topography Transfer learning water Water resources China SWAT LSTM Hydrological modeling Transfer learning K-nearest neighbor
ISSN:	0920-4741, 1573-1650
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