Title :
Visual Data Analysis as an Integral Part of Environmental Management
Author :
Meyer, Joerg ; Bethel, E. Wes ; Horsman, Jennifer L. ; Hubbard, Susan S. ; Krishnan, Harinarayan ; Romosan, Alexandru ; Keating, Elizabeth H. ; Monroe, Laura ; Strelitz, Richard ; Moore, Phil ; Taylor, Glenn ; Torkian, Ben ; Johnson, Timothy C. ; Gorton,
Author_Institution :
Lawrence Berkeley Nat. Lab., Berkeley, CA, USA
Abstract :
The U.S. Department of Energy´s (DOE) Office of Environmental Management (DOE/EM) currently supports an effort to understand and predict the fate of nuclear contaminants and their transport in natural and engineered systems. Geologists, hydrologists, physicists and computer scientists are working together to create models of existing nuclear waste sites, to simulate their behavior and to extrapolate it into the future. We use visualization as an integral part in each step of this process. In the first step, visualization is used to verify model setup and to estimate critical parameters. High-performance computing simulations of contaminant transport produces massive amounts of data, which is then analyzed using visualization software specifically designed for parallel processing of large amounts of structured and unstructured data. Finally, simulation results are validated by comparing simulation results to measured current and historical field data. We describe in this article how visual analysis is used as an integral part of the decision-making process in the planning of ongoing and future treatment options for the contaminated nuclear waste sites. Lessons learned from visually analyzing our large-scale simulation runs will also have an impact on deciding on treatment measures for other contaminated sites.
Keywords :
contamination; data visualisation; environmental science computing; parallel processing; waste management; DOE/EM; contaminant transport; decision-making process; environmental management; high-performance computing; large-scale simulation; nuclear contaminant; nuclear waste site; parallel processing; visual data analysis; visualization software; Computational modeling; Data models; Data visualization; Environmental management; Google; Monitoring; Pollution measurement; Visual analytics; Visual analytics; data management; environmental management; high-performance computing; parallel rendering;
Journal_Title :
Visualization and Computer Graphics, IEEE Transactions on
DOI :
10.1109/TVCG.2012.278