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| Titel |
Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France) |
| VerfasserIn |
G. Artigue, A. Johannet, V. Borrell, S. Pistre |
| 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 ; 12, no. 11 ; Nr. 12, no. 11 (2012-11-12), S.3307-3324 |
| Datensatznummer |
250011191
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| Publikation (Nr.) |
 copernicus.org/nhess-12-3307-2012.pdf |
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| Zusammenfassung |
| In southern France, flash flood episodes frequently cause fatalities and
severe damage. In order to inform and warn populations, the French flood
forecasting service (SCHAPI, Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations)
initiated the BVNE (Bassin Versant Numérique Expérimental, or Experimental Digital
Basin) project in an effort to enhance flash flood predictability. The
target area for this study is the Gardon d'Anduze basin, located in the heart of the
Cévennes range. In this Mediterranean mountainous setting, rainfall intensity can be
very high, resulting in flash flooding. Discharge and rainfall gauges are
often exposed to extreme weather conditions, which undermines measurement
accuracy and continuity. Moreover, the processes governing
rainfall-discharge relations are not well understood for these
steeply-sloped and heterogeneous basins. In this context of inadequate
information on both the forcing variables and process knowledge, neural
networks are investigated due to their universal approximation and parsimony
properties. We demonstrate herein that thanks to a rigorous variable and
complexity selection, efficient forecasting of up to two-hour durations,
without requiring rainfall forecasting as input, can be derived using the
measured discharges available from a feedforward model. In the case of
discharge gauge malfunction, in degraded mode, forecasting may result using
a recurrent neural network model. We also observe that neural network models
exhibit low sensitivity to uncertainty in rainfall measurements since
producing ensemble forecasting does not significantly affect forecasting
quality. In providing good results, this study suggests close consideration
of our main purpose: generating forecasting on ungauged basins. |
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