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Author Kollat, Joshua Brian
Title Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics
book jacket
Descript 211 p
Note Source: Dissertation Abstracts International, Volume: 71-09, Section: B, page: 5653
Adviser: Patrick M. Reed
Thesis (Ph.D.)--The Pennsylvania State University, 2010
This dissertation contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring (LTGM) decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The new framework combines Monte Carlo based contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF), many-objective evolutionary optimization, and visual analytics-based decision support. The ASSIST framework allows decision makers to forecast the value of investments in new observations for many objectives simultaneously. Information tradeoffs are evaluated using an EnKF to forecast plume transport in space and time in the presence of uncertain and biased model predictions that are conditioned on uncertain measurement data. The goal of the ASSIST framework is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design
Each chapter of this dissertation focuses on and addresses a specific challenge to LTGM network design. The scaling challenges of LTGM design are first explored in order to provide a basis for advancing the size and scope of LTGM design problems that can be effectively solved using multi-objective evolutionary algorithms (MOEAs). In addition, complex decision variable interdependencies that exist in large LTGM design problems cause traditional MOEAs to fail as problem sizes increase (defined in terms of increasing numbers of decisions and objectives). To address this, a new more robust MOEA termed the Epsilon-Dominance Hierarchical Bayesian Optimization Algorithm (epsilon-hBOA) was developed to learn and exploit the complex interdependencies that exist for large LTGM design problems. Building on the scalable many-objective optimization capabilities of epsilon-hBOA, the ASSIST framework contributes visual analytical tools, capable of providing decision makers with an improved understanding of the complex spatial and temporal tradeoffs that often exist between their LTGM design objectives. Finally, a bias-aware EnKF framework was developed that dramatically enhances the accuracy of groundwater flow-and-transport forecasts in the presence of systematic modeling errors (or biases), while making computational innovations that again expand the size and scope of LTGM problems that can be addressed
This dissertation demonstrates that the forecasting, search, and visualization components of the ASSIST framework combine to represent a significant advance for LTGM network design that has a strong potential to innovate our future characterization, prediction, and management of groundwater systems
School code: 0176
Host Item Dissertation Abstracts International 71-09B
Subject Hydrology
Engineering, Civil
Water Resource Management
0388
0543
0595
Alt Author The Pennsylvania State University
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