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Communication dans un congrès
Ensemble model to enhance robustness of flash flood forecasting using an Artificial Neural Network: case-study on the Gardon Basin (south-eastern France)
Abstract : During the last few decades neural networks have been increasingly used in hydrological modelling for their
fundamental property of parsimony and of universal approximation of non-linear functions. For the purpose
of flash flood forecasting, feed-forward and recurrent multi-layer perceptrons appear to be efficient tools.
Nevertheless, their forecasting performances are sensitive to the initialization of the network parameters. We
have studied the cross-validation efficiency to select initialization providing the best forecasts in real time situation.
Sensitivity to initialization of feed-forward and recurrent models is compared for one-hour lead-time
forecasts. This study shows that cross-validation is unable to select the best initialization. A more robust
model has been designed using the median of several models outputs; in this context, this paper analyses
the design of the ensemble model for both recurrent and feed-forward models.
https://hal.umontpellier.fr/hal-02128571
Contributeur : Pascale Roussel <>
Soumis le : mardi 14 mai 2019 - 13:44:22
Dernière modification le : mercredi 24 juin 2020 - 16:18:42
Contributeur : Pascale Roussel <>
Soumis le : mardi 14 mai 2019 - 13:44:22
Dernière modification le : mercredi 24 juin 2020 - 16:18:42