Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

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@Article{OUYANG:2017:JCHa,

• author = "Qi Ouyang and Wenxi Lu and Jin Lin and Wenbing Deng and Weiguo Cheng",
• title = "Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at {DNAPLs-contaminated} sites",
• journal = "Journal of Contaminant Hydrology",
• volume = "203",
• pages = "1--8",
• year = "2017",
• keywords = "genetic algorithms, genetic programming, Conservative strategy, Groundwater remediation, Optimization, Surrogate, Uncertainty",
• ISSN = "0169-7722",
• DOI = "doi:10.1016/j.jconhyd.2017.05.007",
• URL = "http://www.sciencedirect.com/science/article/pii/S0169772216302984",
• 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