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A probabilistic wavelet-support vector regression model for streamflow forecasting with rainfall and climate information input

It is essential to explore reliable streamflow forecasting techniques for water resources management. In this study, a Bayesian wavelet-support vector regression model (BWS model) is developed for one- and multistep-ahead streamflow forecasting using local meteohydrological observations and climate...

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Hauptverfasser: Liu, Zhiyong (VerfasserIn) , Zhou, Ping (VerfasserIn) , Zhang, Yinqin (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 05 October 2015
In: Journal of hydrometeorology
Year: 2015, Jahrgang: 16, Heft: 5, Pages: 2209-2229
ISSN:1525-755X
DOI:10.1175/JHM-D-14-0210.1
Online-Zugang:Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1175/JHM-D-14-0210.1
Verlag, lizenzpflichtig, Volltext: https://journals.ametsoc.org/jhm/article/16/5/2209/69932/A-Probabilistic-Wavelet-Support-Vector-Regression
Volltext
Verfasserangaben:Zhiyong Liu, Ping Zhou, Yinqin Zhang
Beschreibung
Zusammenfassung:It is essential to explore reliable streamflow forecasting techniques for water resources management. In this study, a Bayesian wavelet-support vector regression model (BWS model) is developed for one- and multistep-ahead streamflow forecasting using local meteohydrological observations and climate indices including El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) as potential predictors.
Beschreibung:Gesehen am 29.06.2020
Beschreibung:Online Resource
ISSN:1525-755X
DOI:10.1175/JHM-D-14-0210.1

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