Application of Multimodal Optimization for Uncertainty Estimation of Computationally Expensive Hydrologic Models
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 3
Abstract
The generalized likelihood uncertainty estimation (GLUE) framework has been widely used in hydrologic studies. However, the extensive random sampling causes a high computational burden that prohibits the efficient application of GLUE to costly distributed hydrologic models such as the soil and water assessment tool (SWAT). In this study, a multimodal optimization algorithm called isolated-speciation-based particle swarm optimization (ISPSO) is employed to take samples from the search space. A comparison between the ISPSO-GLUE, proposed here, and traditional GLUE approaches shows that the two approaches generate similar uncertainty bounds, but that the convergence rate to stable uncertainty bounds is much faster for ISPSO-GLUE than for GLUE. That is, ISPSO-GLUE needs a much smaller number of samples than GLUE to arrive at a very similar answer. Although ISPSO-GLUE slightly underestimated the prediction uncertainty and missed a number of observed values, the proposed approach is considered to be a good alternative to the typical GLUE approach that employs random sampling.
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Acknowledgments
This study was supported in part by the Ministry of Science and Technology of the Korean Government through the Korea Science and Engineering Foundation grant M06-2003-000-10064-0. The authors would like to thank the manuscript reviewers for their valuable comments and suggestions.
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Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 3 • March 2014
Pages: 313 - 321
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Sep 5, 2011
Accepted: Nov 5, 2012
Published online: Nov 7, 2012
Discussion open until: Apr 7, 2013
Published in print: Mar 1, 2014
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