Streamflow Forecasting Using Different Artificial Neural Network Algorithms
Publication: Journal of Hydrologic Engineering
Volume 12, Issue 5
Abstract
Forecasts of future events are required in many activities associated with planning and operation of the components of a water resources system. For the hydrologic component, there is a need for both short term and long term forecasts of streamflow events in order to optimize the system or to plan for future expansion or reduction. This paper presents a comparison of different artificial neural networks (ANNs) algorithms for short term daily streamflow forecasting. Four different ANN algorithms, namely, backpropagation, conjugate gradient, cascade correlation, and Levenberg–Marquardt are applied to continuous streamflow data of the North Platte River in the United States. The models are verified with untrained data. The results from the different algorithms are compared with each other. The correlation analysis was used in the study and found to be useful for determining appropriate input vectors to the ANNs.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 12 • Issue 5 • September 2007
Pages: 532 - 539
Copyright
© 2007 ASCE.
History
Received: Jul 27, 2005
Accepted: Dec 5, 2006
Published online: Sep 1, 2007
Published in print: Sep 2007
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