Parallel Inverse Modeling and Uncertainty Quantification for Computationally Demanding Groundwater-Flow Models Using Covariance Matrix Adaptation
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 8
Abstract
This study investigates the performance of the covariance matrix adaptation-evolution strategy (CMA-ES), a stochastic optimization method, in solving groundwater inverse problems. The objectives of the study are to evaluate the computational efficiency of the parallel CMA-ES and to investigate the use of the empirically estimated covariance matrix in quantifying model prediction uncertainty due to parameter estimation uncertainty. First, the parallel scaling with increasing number of processors up to a certain limit is discussed for synthetic and real-world groundwater inverse problems. Second, through the use of the empirically estimated covariance matrix of parameters from the CMA-ES, the study adopts the Monte Carlo simulation technique to quantify model prediction uncertainty. The study shows that the parallel CMA-ES is an efficient and powerful method for solving the groundwater inverse problem for computationally demanding groundwater flow models and for deriving covariances of estimated parameters for uncertainty analysis.
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Acknowledgments
This study was supported in part by the National Science Foundation under Grant No. EAR-1045064, the U.S. Geological Survey under Grant/Cooperative Agreement No. G10AP00136, and LSU Economic Development Assistantship (EDA). The authors acknowledge computing resources from the LSU High Performance Computing. The authors thank two anonymous reviewers for their helpful comments.
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Journal of Hydrologic Engineering
Volume 20 • Issue 8 • August 2015
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© 2014 American Society of Civil Engineers.
History
Received: Jun 29, 2014
Accepted: Oct 14, 2014
Published online: Nov 17, 2014
Discussion open until: Apr 17, 2015
Published in print: Aug 1, 2015
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