Fuzzy Neural Network Model for Hydrologic Flow Routing
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 4
Abstract
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 FNN 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 Brahmaputra using flow data at various gauged sites in India. The advantages of using the FNN model in river flow prediction are discussed using the case study.
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Acknowledgment
The writers wish to acknowledge the support given by the Assam State Flood Control Department by providing the necessary data for the analysis.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 10 • Issue 4 • July 2005
Pages: 302 - 314
Copyright
© 2005 ASCE.
History
Received: Sep 9, 2002
Accepted: Aug 12, 2004
Published online: Jul 1, 2005
Published in print: Jul 2005
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