Combining Rainfall-Runoff Model Outputs for Improving Ensemble Streamflow Prediction
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 6
Abstract
This study reviewed various combining methods that have been commonly used in economic forecasting, and examined their applicability in hydrologic forecasting. The following combining methods were investigated: The simple average, constant coefficient regression, switching regression, sum of squared error, and artificial neural network combining methods. Each method combines ensemble streamflow prediction (ESP) scenarios of the existing rainfall-runoff model, TANK, those of the new rainfall-runoff model that has been developed using an ensemble neural network for forecasting the monthly inflow to the Daecheong multipurpose dam in Korea. In addition to the combining, the ESP scenarios were adjusted using correction methods, such as optimal linear and artificial neural network correction methods. Among the tested combining methods, sum of squared error (SSE), a combining method using time-varying weights, performed best with respect to the root mean square error. When SSE was coupled with optimal linear correction (OLC), denoted SSE/OLC, its bias became sufficiently close to zero. SSE/OLC also considerably improved the probabilistic forecasting accuracy of the existing ESP system.
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Acknowledgments
This research was supported by a grant (code 1-6-1) from Sustainable Water Resources Research Center of 21st Century Frontier Research Program and was also supported by the Engineering Research Institute, Seoul National University, Seoul, Korea.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 11 • Issue 6 • November 2006
Pages: 578 - 588
Copyright
© 2006 ASCE.
History
Received: Oct 1, 2004
Accepted: Nov 15, 2005
Published online: Nov 1, 2006
Published in print: Nov 2006
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