Optimal design of groundwater pollution monitoring network under uncertainty
DONG Guang-qi1, LU Wen-xi1, FAN Yue2, PAN Zi-Dong1
1. College of New Energy and Environment, Jilin University, Changchun 130012, China; 2. Yangtze River Scientific Research Institute, Wuhan 430010, China
Abstract：When applying the simulation-optimization method, objective parameter uncertainty will usually affect the reliability of the design result of groundwater pollution monitoring network. For this problem, the study simultaneously considered the uncertainty of hydraulic conductivity and emission intensity of pollution source, applying Monte Carlo method to design the optimal monitoring wells scheme under the influence of model uncertainty. But Monte Carlo method need to invoke the simulation model many times which will cause a huge amount of calculation. To reduce the calculation load, the study proposed to use Extreme Gradient Boosting (XGBoost) method to construct the surrogate model replacing the simulation model to couple the optimization model in the optimal design of GPMN. To sufficiently improve the monitoring precision of GPMN, the optimization model applied error of spatial moment as objective function. Besides, the dynamic change of emission intensity of pollution source was also considered. Finally, we proposed a hypothetical example based on a coal gangue pile in Fushun City to verify the validity of the method. The results are demonstrated: 1.the XGBoost surrogate method can fit the input-output relationship of the simulation model to a high degree with less computation. 2.spatial moment can effectively assess the approximation degree between interpolation pollution plume of GPMN and actual pollution plume, through which the optimized monitoring network can accurately depict actual pollution plume 3.the simulation-optimization method combines Monte Carlo method can solve the problem of the design of GPMN under uncertainty. In conclusion, the paper provides a stable and reliable method for the design of GPMN.
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