Groundwater Remediation Design under Uncertainty Using Genetic Algorithms

Abstract

In groundwater problems, there always is some uncertainty associated with appropriate values for aquifer parameters. Therefore an optimal remediation strategy identified by assuming a deterministic description of the system may not yield an optimal and feasible design. This work develops a robust genetic algorithm (GA) approach that takes into account the uncertainty of hydraulic conductivity values when determining the best remediation design possible. Within a generation of the robust GA, all designs are evaluated using the same realization of the heterogeneous hydraulic conductivity field, but the realizations vary between GA generations. Ongoing performance of the designs is measured and is used in the GA evolution process. While the robust GA is a multiple realization method, minimal additional computation effort over that of a basic GA is required to identify robust designs. The robust GA is applied to two cases of varying heterogeneity of an example contaminated aquifer remediated by a pump-and-treat system. The goal of the optimization problem is to identify low cost designs that meet constraints on contaminant concentrations, hydraulic heads, and well extraction rates given uncertainty in hydraulic conductivity values. In addition, the basic GA and noisy GA are applied to the same problem for comparison. The robust GA identified designs with lower costs and using fewer objective function evaluations than the noisy GA designs. However, the noisy GA designs generally have lower concentration constraint violation and higher reliability than the robust GA designs, particularly in the more heterogeneous case. As heterogeneity and uncertainty in hydraulic conductivity increases, remediation costs increase as more wells are required to satisfy the remediation goal and reliability decreases. Nevertheless, both the robust GA and noisy GA identified designs that performed better in constraint feasibility and reliability than the solutions found by the basic GA, which assumed deterministic conditions. Additionally, this work shows that assuming a deterministic description of the aquifer—either homogeneous or heterogeneous—can result in significant underdesign and poor remediation performance.