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| Titel |
Simulation of flood flow in a river system using artificial neural networks |
| VerfasserIn |
R. R. Shrestha, S. Theobald, F. Nestmann |
| 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 ; 9, no. 4 ; Nr. 9, no. 4 (2005-10-07), S.313-321 |
| Datensatznummer |
250006965
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| Publikation (Nr.) |
 copernicus.org/hess-9-313-2005.pdf |
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| Zusammenfassung |
| Artificial neural networks (ANNs) provide a quick and flexible means of
developing flood flow simulation models. An important criterion for the wider
applicability of the ANNs is the ability to generalise the events outside the
range of training data sets. With respect to flood flow simulation, the
ability to extrapolate beyond the range of calibrated data sets is of crucial
importance. This study explores methods for improving generalisation of the
ANNs using three different flood events data sets from the Neckar River in
Germany. An ANN-based model is formulated to simulate flows at certain
locations in the river reach, based on the flows at upstream locations.
Network training data sets consist of time series of flows from observation
stations. Simulated flows from a one-dimensional hydrodynamic numerical model
are integrated for network training and validation, at a river section where
no measurements are available. Network structures with different activation
functions are considered for improving generalisation. The training algorithm
involved backpropagation with the Levenberg-Marquardt approximation. The
ability of the trained networks to extrapolate is assessed using flow data
beyond the range of the training data sets. The results of this study
indicate that the ANN in a suitable configuration can extend forecasting
capability to a certain extent beyond the range of calibrated data sets. |
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