Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Accurate forecast of short-term to long-term streamflow prediction is of great importance for water resources management. However, with the advent of novel hybrid machine learning methods, it remains unclear whether these hybrid models can outperform the traditional streamflow forecast models. There...

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Veröffentlicht in:	Water resources management Jg. 35; H. 4; S. 1149 - 1166
Hauptverfasser:	Riahi-Madvar, Hossien, Dehghani, Majid, Memarzadeh, Rasoul, Gharabaghi, Bahram
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2021 Springer Nature B.V
Schlagworte:	administrative management Algorithms Atmospheric Sciences Civil Engineering Damsites Earth and Environmental Science Earth Sciences Environment equations Evolutionary algorithms Evolutionary computation Forecasting Genetic algorithms Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology/Water Resources Inflow Iran Learning algorithms Machine learning Mathematical models model validation Monte Carlo method Monte Carlo simulation Optimization Particle swarm optimization prediction River flow River forecasting rivers Statistical methods Stream discharge Stream flow Streamflow forecasting Uncertainty Uncertainty analysis water Water resources Water resources management Weekly Iran Explicit estimation Karun River Uncertainty analysis Streamflow Prediction horizon Monte Carlo simulation
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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