Forecasting of Monthly Streamflows Based on Artificial Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 12
Abstract
Artificial neural networks (ANN) have experienced a major breakthrough in civil engineering topics throughout the past 15 years, especially in the hydroinformatics field. Fewer attempts have been made to unveil any feasible physical meaning behind the ANN and their probable application for solving day to day engineering problems. This work explores the possibility of linking the weights of simple multilayer perceptrons with some physical characteristics of watersheds, by means of statistical regressions. The procedure is applied to the forecast of monthly streamflows in the central region of Colombia. Nineteen watersheds were delimited within the zone of study, using geographic information system software. Obtained results allow to foresee that watersheds characteristics such as area, length, and slope of the main stream could be connected with the ANN weights. Better results are expected when daily records and other variables such as rain, evaporation, etc. be included.
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Acknowledgments
The writers are indebted to Professor Mario Diaz-Granados from Universidad de Los Andes for his thoughtful and timely contributions during the development of this project.
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Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 12 • December 2009
Pages: 1390 - 1395
Copyright
© 2009 ASCE.
History
Received: Jul 7, 2006
Accepted: Jun 22, 2009
Published online: Nov 13, 2009
Published in print: Dec 2009
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