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| Titel |
A non-linear neural network technique for updating of river flow forecasts |
| VerfasserIn |
A. Y. Shamseldin, K. M. O'Connor |
| 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 ; 5, no. 4 ; Nr. 5, no. 4, S.577-598 |
| Datensatznummer |
250002710
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| Publikation (Nr.) |
 copernicus.org/hess-5-577-2001.pdf |
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| Zusammenfassung |
| A non-linear
Auto-Regressive Exogenous-input model (NARXM) river flow forecasting
output-updating procedure is presented. This updating procedure
is based on the structure of a multi-layer neural network. The NARXM-neural
network updating procedure is tested using the daily discharge
forecasts of the soil moisture accounting and routing (SMAR) conceptual model
operating on five catchments having different climatic
conditions. The performance of the NARXM-neural network updating procedure is
compared with that of the linear Auto-Regressive Exogenous-input
(ARXM) model updating procedure, the latter being a generalisation of the widely
used Auto-Regressive (AR) model forecast error updating
procedure. The results of the comparison indicate that the NARXM procedure
performs better than the ARXM procedure.
Keywords: Auto-Regressive Exogenous-input model, neural network,
output-updating procedure, soil moisture accounting and routing (SMAR) model |
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