A fuzzy neural network model for deriving the river stage—discharge relationship
Open Access
- 1 April 2003
- journal article
- Published by Informa UK Limited in Hydrological Sciences Journal
- Vol. 48 (2), 197-209
- https://doi.org/10.1623/hysj.48.2.197.44697
Abstract
The measurement of discharge in major rivers is very important and serves as the base information for hydrological analysis. The rating curve is used to assess the discharge from the measured stage values in the gauging sites. The rating curve has important bearing on the correct assessment of discharge. The usefulness of the fuzzy neural network modelling approach in deriving the stage—discharge relationship is discussed. The performances of a neural network model, a modularized neural network model, a conventional curve-fitting approach and a fuzzy neural network model for deriving the rating curve are compared using a case study. Overall, the fuzzy neural network model gives the best results. La mesure du débit dans les rivières est très importante et constitue l'information de base des analyses hydrologiques. La courbe de tarage d'une station de jaugeage sert à connaître le débit à partir des valeurs mesurées de hauteur d'eau. Nous discutons de l'utilité d'une approche de modélisation par réseau de neurones flou pour établir une courbe de tarage. Les performances d'un modèle de type réseau de neurones, d'un modèle de type réseau de neurones modulaire, d'une approche conventionnelle d'ajustement de loi et d'un modèle de type réseau de neurones flou sont comparées pour l'établissement de la courbe de tarage d'un cas d'étude. Manifestement, le modèle de type réseau de neurones flou fournit les meilleurs résultats.Keywords
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