Fuzzy Neural Network Model for Hydrologic Flow Routing / Deka, Paresh in Journal of hydrologic engineering, Vol. 10, N°4 (Juillet/Août 2005)

Public ISBD [article]  inJournal of hydrologic engineering > Vol. 10, N°4 (Juillet/Août 2005) . - 302-314 p. Titre : Fuzzy Neural Network Model for Hydrologic Flow Routing Type de document : texte imprimé Auteurs : Deka, Paresh, Auteur ; Chandramouli, V., Auteur Année de publication : 2006 Article en page(s) : 302-314 p. Note générale : Hydrologie Langues : Anglais (eng) Mots-clés : Fuzzy sets Neural networks River flow Routing Index. décimale : 551.4 surface du globe.Géographie physique.Géomorphologie Résumé : This Paper presents a new approach to river flow prediction using a fuzzy neural network (FNN) model. An FNN combines the learning ability of artificial neural networks with the merits of fuzzy logic. The FUNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The Model displays the stored knowledge in terms of fuzzy linguistic rules, which allows the model decision-making process to be examined and understood in detail. The FNN model is tested on the river Brahmaptura using flow data at various gauged sites in India. The Advantage of using the FNN model in river flow prediction are discussed using the case study. [article] Fuzzy Neural Network Model for Hydrologic Flow Routing [texte imprimé] / Deka, Paresh, Auteur ; Chandramouli, V., Auteur . - 2006 . - 302-314 p. Hydrologie Langues : Anglais (eng) in Journal of hydrologic engineering > Vol. 10, N°4 (Juillet/Août 2005) . - 302-314 p. Mots-clés : Fuzzy sets Neural networks River flow Routing Index. décimale : 551.4 surface du globe.Géographie physique.Géomorphologie Résumé : This Paper presents a new approach to river flow prediction using a fuzzy neural network (FNN) model. An FNN combines the learning ability of artificial neural networks with the merits of fuzzy logic. The FUNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The Model displays the stored knowledge in terms of fuzzy linguistic rules, which allows the model decision-making process to be examined and understood in detail. The FNN model is tested on the river Brahmaptura using flow data at various gauged sites in India. The Advantage of using the FNN model in river flow prediction are discussed using the case study.

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