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| Titel |
Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database |
| VerfasserIn |
T.-Y. Pan, J.-S. Lai, T.-J. Chang, H.-K. Chang, K.-C. Chang, Y.-C. Tan |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 11, no. 3 ; Nr. 11, no. 3 (2011-03-11), S.771-787 |
| Datensatznummer |
250009259
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| Publikation (Nr.) |
 copernicus.org/nhess-11-771-2011.pdf |
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| Zusammenfassung |
| This study attempts to achieve real-time rainfall-inundation forecasting in
lowland regions, based on a synthetic potential inundation database. With the
principal component analysis and a feed-forward neural network, a
rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast
1-h-ahead inundation depth as hydrographs at specific representative
locations using spatial rainfall intensities and accumulations. A systematic
procedure is presented to construct the RiHNN, which combines the merits of
detailed hydraulic modeling in flood-prone lowlands via a two-dimensional
overland-flow model and time-saving calculation in a real-time
rainfall-inundation forecasting via ANN model. Analytical results from the
RiHNNs with various principal components indicate that the RiHNNs with fewer
weights can have about the same performance as a feed-forward neural network.
The RiHNNs evaluated through four types of real/synthetic rainfall events
also show to fit inundation-depth hydrographs well with high rainfall.
Moreover, the results of real-time rainfall-inundation forecasting help the
emergency manager set operational responses, which are beneficial for flood
warning preparations. |
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