A General Simulation-optimization Approach for Groundwater Sampling Network Design

摘要

A new simulation-optimization methodology is developed for cost-effective sampling network design associated with monitoring of contaminant plumes. The methodology is modified from the one presented by Reed et al. in which an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and a global mass estimator to search for optimal sampling strategies. This study introduces the first and second moments of a contaminant plume as new constraints in the optimization formulation, and demonstrates the efficacy of the proposed methodology through a hypothetical application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approach employed in this study is ordinary kriging (OK). The proposed methodology is applied to the monitoring of a spill plume evolution. It is shown that potential cost savings of up to 53.5% may be achieved without any significant loss of accuracy in mass and moment estimations. Additional comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust monitoring design that is both cost-effective and accurate in mass and moment estimations.