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| Titel |
Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models |
| VerfasserIn |
A. Brath, A. Montanari, E. Toth |
| 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 ; 6, no. 4 ; Nr. 6, no. 4, S.627-639 |
| Datensatznummer |
250003662
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| Publikation (Nr.) |
 copernicus.org/hess-6-627-2002.pdf |
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| Zusammenfassung |
| Time-series analysis
techniques for improving the real-time flood forecasts issued by a deterministic
lumped rainfall-runoff model are presented. Such techniques are applied for
forecasting the short-term future rainfall to be used as real-time input in a
rainfall-runoff model and for updating the discharge predictions provided by the
model. Along with traditional linear stochastic models, both stationary (ARMA)
and non-stationary (ARIMA), the application of non-linear time-series models is
proposed such as Artificial Neural Networks (ANNs) and the ‘nearest-neighbours’
method, which is a non-parametric regression methodology.
For both rainfall forecasting and discharge updating, the implementation of each
time-series technique is investigated and the forecasting schemes which perform
best are identified. The performances of the models are then compared and the
improvement in the efficiency of the discharge forecasts achievable is
demonstrated when i) short-term rainfall forecasting is performed, ii) the
discharge is updated and iii) both rainfall forecasting and discharge updating
are performed in cascade. The proposed techniques, especially those based on
ANNs, allow a remarkable improvement in the discharge forecast, compared with
the use of heuristic rainfall prediction approaches or the not-updated discharge
forecasts given by the deterministic rainfall-runoff model alone.
Keywords: real-time flood forecasting, precipitation prediction,
discharge updating, time-series analysis techniques |
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