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| Titel |
Online multistep-ahead inundation depth forecasts by recurrent NARX networks |
| VerfasserIn |
H.-Y. Shen, L.-C. Chang |
| 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. 3 ; Nr. 17, no. 3 (2013-03-05), S.935-945 |
| Datensatznummer |
250018814
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| Publikation (Nr.) |
 copernicus.org/hess-17-935-2013.pdf |
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| Zusammenfassung |
| Various types of artificial neural networks (ANNs) have been successfully
applied in hydrological fields, but relatively scant on multistep-ahead
flood inundation forecasting, which is very difficult to achieve,
especially when dealing with forecasts without regular observed data. This study
proposes a recurrent configuration of nonlinear autoregressive with
exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead
inundation depths in an inundation area. The proposed R-NARX is constructed
based on the recurrent neural network (RNN), which is commonly used for
modeling nonlinear dynamical systems. The models were trained and tested
based on a large number of inundation data generated by a well validated
two-dimensional simulation model at thirteen inundation-prone sites in Yilan
County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit
error growth and accumulation when being applied to online multistep-ahead
inundation forecasts over a long lasting forecast period. For comparison, a
feedforward time-delay and an online feedback configuration of NARX
networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX
networks cannot make online forecasts due to unavailable inputs in
the constructed networks even though they provide the best performances for
reference only; and (2) R-NARX networks consistently outperform O-NARX
networks and can be adequately applied to online multistep-ahead forecasts
of inundation depths in the study area during typhoon events. |
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