Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting

Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gai...

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Veröffentlicht in:	Water resources management Jg. 35; H. 12; S. 4167 - 4187
Hauptverfasser:	Rahimzad, Maryam, Moghaddam Nia, Alireza, Zolfonoon, Hosam, Soltani, Jaber, Danandeh Mehr, Ali, Kwon, Hyun-Han
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.09.2021 Springer Nature B.V
Schlagworte:	Algorithms Atmospheric Sciences Civil Engineering Deep learning Earth and Environmental Science Earth Sciences Environment Errors Flood forecasting Flood management Forecasting Geotechnical Engineering & Applied Earth Sciences High flow Hydrogeology Hydrology Hydrology/Water Resources Kentucky Kentucky River Learning algorithms Long short-term memory Low flow Machine learning Mathematical models Maximum flow Mitigation Multilayer perceptrons neural networks regression analysis Resource allocation River basins Statistical analysis Statistical methods Stream discharge Stream flow Streamflow forecasting Support vector machines time series analysis water Water resources watersheds Kentucky River Kentucky Data-driven modeling Deep learning Machine learning algorithms Streamflow forecasting LSTM
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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