GROUNDWATER MONITORING DESIGN: A CASE STUDY COMBINING EPSILON DOMINANCE ARCHIVING AND AUTOMATIC PARAMETERIZATION FOR THE NSGA-II

The monitoring design problem is extremely challenging because it requires environmental engineers to capture an impacted system's governing processes, elucidate human and ecologic risks, limit monitoring costs, and satisfy the interests of multiple stakeholders (e.g., site owners, regulators, and public advocates). Evolutionary multiobjective optimization (EMO) has tremendous potential to help resolve these issues by providing environmental stakeholders with a direct understanding of their monitoring tradeoffs. This chapter demonstrates the use of the Nondom-inated Sorted Genetic Algorithm-II (NSGA-II) to optimize groundwater monitoring networks for conflicting objectives. Additionally, this chapter demonstrates how ε-dominance archiving and automatic parameterization techniques for the NSGA-II can be used to significantly improve the algorithm's ease-of-use and efficiency for computationally intensive applications. Results are presented for a 2-objective groundwater monitoring case study in which the archiving and parameterization techniques for the NSGA-II combined to reduce computational demands by greater than 70-percent relative to prior published results. The methods of this chapter can be easily generalized to other multiobjective applications to minimize computational times as well as trial-and-error EMO parameter analysis.