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| Titel |
Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet neural network hybrid model |
| VerfasserIn |
J.-S. Yang, S.-P. Yu, G.-M. Liu |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 12 ; Nr. 17, no. 12 (2013-12-10), S.4981-4993 |
| Datensatznummer |
250086026
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| Publikation (Nr.) |
 copernicus.org/hess-17-4981-2013.pdf |
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| Zusammenfassung |
| In order to increase the accuracy of serial-propagated
long-range multi-step-ahead (MSA) prediction, which has high practical value
but also great implementary difficulty because of huge error accumulation, a
novel wavelet neural network hybrid model – CDW-NN – combining continuous and discrete
wavelet transforms (CWT and DWT) and neural networks (NNs), is designed as
the MSA predictor for the effective long-term forecast of hydrological
signals. By the application of 12 types of hybrid and pure models in
estuarine 1096-day river stages forecasting, the different forecast
performances and the superiorities of CDW-NN model with corresponding
driving mechanisms are discussed. One type of CDW-NN model, CDW-NF, which
uses neuro-fuzzy as the forecast submodel, has been proven to be the most
effective MSA predictor for the prominent accuracy enhancement during the
overall 1096-day long-term forecasts. The special superiority of CDW-NF
model lies in the CWT-based methodology, which determines the 15-day and
28-day prior data series as model inputs by revealing the significant
short-time periodicities involved in estuarine river stage signals.
Comparing the conventional single-step-ahead-based long-term forecast
models, the CWT-based hybrid models broaden the prediction range in each
forecast step from 1 day to 15 days, and thus reduce the overall forecasting
iteration steps from 1096 steps to 74 steps and finally create significant
decrease of error accumulations. In addition, combination of the advantages
of DWT method and neuro-fuzzy system also benefits filtering the noisy
dynamics in model inputs and enhancing the simulation and forecast ability
for the complex hydro-system. |
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