Forecasting Concurrent Flows in a River System Using ANNs
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 8
Abstract
Applications of artificial neural networks (ANNs) in forecasting flow rates at multiple sections in a river system are presented. Model formulations are based on learning characteristics of the actual and fractional storage variations in river reaches during unsteady flow. Multilayer perceptrons (MLP), MLPs with memory, time delay neural networks (TDNNs), and multiple gamma memory neural networks in three model forms are used to forecast flow rates in Tar River Basin, United States. Model performances are evaluated in terms of statistical criteria, RMS error, and coefficient of efficiency. Maximum RMS error resulted for the models are less than 6.50% of the respective observed mean value. A coefficient of efficiency value of more than 0.95 for the models indicates satisfactory performances. Results presented in this paper depict flow variations corresponding to implicitly specified storage variations and demonstrate applicability of the ANNs in real time flow forecasting for multiple sections in a basin obeying continuity principle.
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Published In
Journal of Hydrologic Engineering
Volume 20 • Issue 8 • August 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Sep 29, 2013
Accepted: Sep 24, 2014
Published online: Oct 28, 2014
Discussion open until: Mar 28, 2015
Published in print: Aug 1, 2015
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