2013: Latin hypercube sampling applied to reliability-based multidisciplinary design optimization of a launch vehicle Aerospace Science and Technology (Imprime) 28(1): 297-304 2018: Kriging and Latin Hypercube Sampling Assisted Simulation Optimization in Optimal Design of PID Controller for Speed Control of DC Motor Journal of Computational and Theoretical Nanoscience 15(5): 1471-1479 2020: Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs Applied Mathematical Modelling 78: 519-538 2019: Model updating of a historic concrete bridge by sensitivity- and global optimization-based Latin Hypercube Sampling Engineering Structures 179: 139-160 2015: Optimization design based on ensemble surrogate models for DNAPLs-contaminated groundwater remediation Journal of Water Supply Research and Technology-Aqua 64(6): 697-707įerrari, R. 2017: Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites Journal of Contaminant Hydrology 203: 1-8Ĭhu, H. Lu, W 2013: Optimization of Denser Nonaqueous Phase Liquids-contaminated groundwater remediation based on Kriging surrogate model Water Practice and Technology 8(2): 304-314 2014: Adaptive Kriging surrogate model for the optimization design of a dense non-aqueous phase liquid-contaminated groundwater remediation process Water Supply 15(2): 263-270 This work would be helpful for increasing surrogate model performance and lowering the risk of a groundwater remediation strategy. (3) The optimal remediation strategies at 99%, 95%, 90%, 85%, 80% and 50% confidence levels were obtained, which showed that the remediation cost increased with the confidence level. The results indicated that the OLHS-based surrogate model performed better than the LHS-based surrogate model. (2) The effects of the two sampling methods on surrogate model performance were analyzed through comparison of goodness of fit, residual and uncertainty. (1) Compared with the Latin hypercube sampling (LHS) method, the OLHS method improves the space-filling degree of sample points considerably. The results showed the following, for the problem considered in this study. Considering the surrogate model's uncertainty, a chance-constrained programming (CCP) model was constructed, and it was solved by genetic algorithm. To illustrate the impact of sampling method improvement to the surrogate model performance improvement, aiming at a nitrobenzene contaminated groundwater remediation problem, optimal Latin hypercube sampling (OLHS) method was introduced to sample data in the input variables feasible region, and a radial basis function artificial neural network was used to construct a surrogate model. A surrogate model based groundwater optimization model was developed to solve the non-aqueous phase liquids (NAPLs) contaminated groundwater remediation optimization problem.
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