Comparison of Surrogate Models Based on Different Sampling Methods for Groundwater Remediation
Publication: Journal of Water Resources Planning and Management
Volume 145, Issue 5
Abstract
To assess the influence of sampling methods on surrogate models’ accuracy, using two test problems and a nitrobenzene-contaminated aquifer remediation problem, several sampling methods were adopted to collect sample data sets and a Kriging method was adopted to construct surrogate models. The sampling methods adopted include Latin hypercube sampling (LHS), space-filling-based LHS (SFLHS), orthogonal-array-based LHS (OALHS), and space-filling and orthogonal-array-based LHS (SFOALHS). The space-filling properties and orthogonality of sampling results, as well as the corresponding surrogate models’ accuracies, were compared, and the best surrogate model was invoked for assessing the remediation efficiency in a groundwater remediation optimization problem. The results indicated that (1) compared with LHS, SFLHS, and OALHS results, the SFOALHS result had the best trade-off between orthogonality and space-filling property, and better represented the population; and (2) the SFOALHS-based surrogate model was more accurate and better fit the simulation model in both test problems and the case study; therefore it was invoked as a constraint condition for replacing the behavior of the computational simulation model in the optimization process.
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Acknowledgments
The research was supported by the National Natural Science Foundation of China (No. 41502221), the China Postdoctoral Science Foundation (No. 2015M570274), the Scientific and Technological Development Program of Jilin Province, China (No. 20180520092JH), and the National Natural Science Foundation of China (No. 41372237). The authors thank the editors and reviewers for the comments and suggestions, which improved the manuscript.
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Published In
Journal of Water Resources Planning and Management
Volume 145 • Issue 5 • May 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Oct 23, 2017
Accepted: Oct 21, 2018
Published online: Mar 6, 2019
Published in print: May 1, 2019
Discussion open until: Aug 6, 2019
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