Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural practices and hydro-power generation. However, the dynamicity, stochasticity and inherent complexities present in the temporal evolution...

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Veröffentlicht in:	Water resources management Jg. 32; H. 5; S. 1883 - 1899
Hauptverfasser:	Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C., El-shafie, Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2018 Springer Nature B.V
Schlagworte:	Agricultural practices Algorithms Artificial neural networks Atmospheric Sciences Autocorrelation Back propagation networks Basins Civil Engineering Correlation Data data collection Data processing Earth and Environmental Science Earth Sciences Electric power generation Environment Evaluation Flood control Flood forecasting Flood management Forecasting Geotechnical Engineering & Applied Earth Sciences Hydroelectric power Hydrogeology Hydrologic data Hydrologic models Hydrology Hydrology/Water Resources Mathematical models Neural networks Parameter uncertainty Recurrent neural networks River water Rivers Rolling (ship motion) Stochasticity Stream discharge Stream flow Streamflow forecasting Training Tropical climate uncertainty Water flow water power Malaysia Rolling mechanism Artificial neural network Multiple time scales Streamflow Grey model Tropical environment
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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