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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Water resources management Jg. 39; H. 9; S. 4493 - 4507
Hauptverfasser:	Xie, Zaichao, Xu, Wei, Zhu, Bing, Yin, Shiming, Yang, Yi, Li, Xiaojie, Wang, Sufan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.07.2025 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!