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-tre...