Journal
JOURNAL OF HYDROLOGIC ENGINEERING
Volume 20, Issue 8, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)HE.1943-5584.0001126
Keywords
Groundwater; Inverse modeling; Stochastic optimization; Covariance matrix; Evolution strategy; Uncertainty quantification; Parallel computing
Funding
- National Science Foundation [EAR-1045064]
- U.S. Geological Survey [G10AP00136]
- LSU Economic Development Assistantship (EDA)
- Directorate For Geosciences
- Division Of Earth Sciences [1045064] Funding Source: National Science Foundation
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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. (C) 2014 American Society of Civil Engineers.
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