Capability of Artificial Neural Network for Detecting Hysteresis Phenomenon Involved in Hydrological Processes
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 5
Abstract
In this paper, artificial neural network (ANN) was applied to model and study the signature of hysteresis phenomena in hydrological processes for the Eel River watershed located in California. Because of the nonlinear and stochastic nature of hysteresis phenomena, it is reasonable to expect ANN to develop a model that efficiently considers hysteretic loops. In this study, hysteretic loops were studied from different aspects such as forms, classification, and effective factors of creation. In rainfall-runoff modeling, counterclockwise loops were mostly observed, whereas in the runoff-sediment process, clockwise loops prevailed. Random or eight-shaped loops were expected in runoff hydrographs with several peaks. A direct relationship was detected between the width of the loops and the area of the subbasin. Larger areas led to wider hysteretic loops. The results showed that ANN efficiently considers hysteresis signs when modeling hydrological processes and can lead to appropriate performance.
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Acknowledgments
This study has been financially supported by a research grant presented by Research Affairs of the University of Tabriz.
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Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 5 • May 2014
Pages: 896 - 906
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jul 18, 2012
Accepted: Jun 25, 2013
Published online: Jun 27, 2013
Discussion open until: Nov 27, 2013
Published in print: May 1, 2014
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