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: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
05 October 2015
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| 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 
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| Verfasserangaben: | Zhiyong Liu, Ping Zhou, Yinqin Zhang |
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| 520 | |a 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. | ||
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