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Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system.

Shu, Chang; Ouarda, Taha B. M. J. (2008). Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology , vol. 349 , nº 1-2. p. 31-43. DOI: 10.1016/j.jhydrol.2007.10.050.

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Résumé

In this paper, the methodology of using adaptive neuro-fuzzy inference systems (ANFIS) for flood quantile estimation at ungauged sites is presented. The proposed approach has the system identification and interpretability of fuzzy models and the learning capability of artificial neural networks (ANNs). The structure of the ANFIS is identified using the subtractive clustering algorithm. A hybrid learning algorithm consisting of back-propagation and least-squares estimation is used for system training. The ANFIS approach provides an integrated mechanism for identifying the hydrological regions, generating knowledge from the data, providing flood estimates and self-tuning to achieve the optimal performance. The proposed approach is applied to 151 catchments in the province of Quebec, Canada, and is compared to the ANN approach, the nonlinear regression (NLR) approach and the nonlinear regression with regionalization approach (NLR-R). A jackknife procedure is used for the evaluation of the performances of the three approaches. Results indicate that the ANFIS approach has a much better generalization capability than the NLR and NLR-R approaches and is comparable to the ANN approach.

Type de document: Article
Mots-clés libres: ANFIS; subtractive clustering; hybrid learning; regionalisation; fuzzy logic; artificial neural network; flood frequency analysis; ungauged
Centre: Centre Eau Terre Environnement
Date de dépôt: 08 janv. 2021 16:47
Dernière modification: 08 janv. 2021 16:47
URI: http://espace.inrs.ca/id/eprint/10829

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