Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites
Created by W.Langdon from
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- @Article{OUYANG:2017:JCHa,
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author = "Qi Ouyang and Wenxi Lu and Jin Lin and
Wenbing Deng and Weiguo Cheng",
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title = "Conservative strategy-based ensemble surrogate model
for optimal groundwater remediation design at
{DNAPLs-contaminated} sites",
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journal = "Journal of Contaminant Hydrology",
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volume = "203",
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pages = "1--8",
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year = "2017",
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keywords = "genetic algorithms, genetic programming, Conservative
strategy, Groundwater remediation, Optimization,
Surrogate, Uncertainty",
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ISSN = "0169-7722",
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DOI = "doi:10.1016/j.jconhyd.2017.05.007",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169772216302984",
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abstract = "The surrogate-based simulation-optimization techniques
are frequently used for optimal groundwater remediation
design. When this technique is used, surrogate errors
caused by surrogate-modeling uncertainty may lead to
generation of infeasible designs. In this paper, a
conservative strategy that pushes the optimal design
into the feasible region was used to address
surrogate-modeling uncertainty. In addition,
chance-constrained programming (CCP) was adopted to
compare with the conservative strategy in addressing
this uncertainty. Three methods, multi-gene genetic
programming (MGGP), Kriging (KRG) and support vector
regression (SVR), were used to construct surrogate
models for a time-consuming multi-phase flow model. To
improve the performance of the surrogate model,
ensemble surrogates were constructed based on
combinations of different stand-alone surrogate models.
The results show that: (1) the surrogate-modeling
uncertainty was successfully addressed by the
conservative strategy, which means that this method is
promising for addressing surrogate-modeling
uncertainty. (2) The ensemble surrogate model that
combines MGGP with KRG showed the most favorable
performance, which indicates that this ensemble
surrogate can use both stand-alone surrogate models to
improve the performance of the surrogate model",
- }
Genetic Programming entries for
Qi Ouyang
Wenxi Lu
Jin Lin
Wenbing Deng
Weiguo Cheng
Citations