Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty.

Abstract

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation. To reduce the calculation load, the study proposed to use the support vector regression (SVR) method to construct the surrogate model to couple the simulation model and the optimization model in the optimal design of GPMN. The optimization goal is to maximize the accuracy of the spatial description of pollution plume in each monitoring period. The study also considered the dynamic changes in the migration and morphological of pollution plumes in the optimization of GPMN. Finally, the West Shechang coal gangue pile in Fushun of China was used as a case study to verify the effectiveness of the above method. The results demonstrate that the SVR surrogate model can fit the input-output relationship of the simulation model to a high degree with less computation. The optimized monitoring network can reveal essential and comprehensive information about pollution plumes. The study provides a stable and reliable method for the design of GPMN.