Striking the Balance: Long-Term Groundwater Monitoring Design for Multiple Conflicting Objectives


This research develops a highly adaptable multiobjective long term monitoring (LTM) design methodology that aids the negotiation process by enabling decision makers to directly assess the tradeoffs among a variety of performance objectives. The monitoring methodology combines quantile kriging and the Nondominated Sorted Genetic Algorithm-II (NSGA-II) to successfully balance four objectives: (1) minimizing sampling costs, (2) maximizing the quality of interpolated plume maps, (3) maximizing the relative accuracy of contaminant mass estimates, and (4) minimizing local estimation uncertainty. Visualization is used as an aid in selecting, understanding, and balancing these performance objectives en route to a single compromise solution. Quantile kriging was selected based on a rigorous study of 6 nonstationary plume interpolation methods for scatter-point concentration data ranging in complexity from intrinsic kriging based on intrinsic random function theory to a traditional implementation of inverse-distance weighting. Quantile kriging was the most robust of the interpolation methods, showing the least bias from the variability of contaminant samples and preferential sampling. Additionally, the method's non-parameric uncertainty estimates successfully predicted zones of high estimation error for each test case. The tradeoffs between the 4 LTM objectives were captured by developing a design methodology for the NSGA-II that uses a multi-population approach to automate parameter specification for the algorithm. The design methodology fully exploits the efficiency of the NSGA-II to enable the solution of a new class of high order multiobjective applications in which users from any discipline seek to select, understand, and balance more than two performance criteria. The LTM design methodology developed in this research addresses the two most important problems LTM practitioners face in the design process: (1) selecting monitoring objectives and (2) balancing these objectives. This thesis demonstrates that combining higher order Pareto optimization (i.e., optimizing a system for more than 2 objectives) with visualization can allow designers in any field to assess the mathematical models used to represent their objectives, discover how their objectives are affecting designs, and negotiate a final design that balances their conflicting design preferences.