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Many-objective groundwater monitorin...
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Kollat, Joshua Brian.
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Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics./
作者:
Kollat, Joshua Brian.
面頁冊數:
211 p.
附註:
Source: Dissertation Abstracts International, Volume: 71-09, Section: B, page: 5653.
Contained By:
Dissertation Abstracts International71-09B.
標題:
Hydrology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3420166
ISBN:
9781124167015
Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics.
Kollat, Joshua Brian.
Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics.
- 211 p.
Source: Dissertation Abstracts International, Volume: 71-09, Section: B, page: 5653.
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.
ISBN: 9781124167015Subjects--Topical Terms:
545716
Hydrology.
Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics.
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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.
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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.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3420166
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