Hydrologic Applications of MRAN Algorithm
Publication: Journal of Hydrologic Engineering
Volume 12, Issue 1
Abstract
Applications of artificial neural networks in simulation and forecasting of hydrologic systems have a long record and generally promising results. Most of the earlier applications were based on the back-propagation (BP) feed-forward method, which used a trial-and-error to determine the final network parameters. The minimal resource allocation network (MRAN) is an on-line adaptive method that automatically configures the number of hidden nodes based on the input–output patterns presented to the network. Numerous MRAN applications in various fields such as system identification and signal processing demonstrated flexibility of the MRAN approach and higher or similar accuracy with more compact networks, compared to other learning algorithms. This research introduces MRAN and assesses its performance in hydrologic applications. The technique was applied to an agricultural watershed in central Illinois to predict daily runoff and nitrate–nitrogen concentration, and the predictions were more accurate compared to the BP model.
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Acknowledgments
The writers appreciate the cooperation of Ms. Laura Keefer, Hydrologist at the Illinois State Water Survey, for providing the data. Partial support for the research was provided by NSF Grant No. NSFEAR 02-08009, NOAA Grant No. UNSPECIFIEDNA03OAR4310070, and NCSA Faculty Fellowship. The writers gratefully acknowledge help and valuable suggestions from Dr. N. Sundararajan, and Dr. P. Saratchandran, Nanyang Technological University, Singapore.
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Published In
Journal of Hydrologic Engineering
Volume 12 • Issue 1 • January 2007
Pages: 124 - 129
Copyright
© 2007 ASCE.
History
Received: May 5, 2004
Accepted: May 19, 2006
Published online: Jan 1, 2007
Published in print: Jan 2007
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