Groundwater remediation optimization using artificial neural networks

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One continuing point of research in optimizing groundwater quality management is reduction of computational burden which is particularly limiting in field-scale applications. Often evaluation of a single pumping strategy, i.e. one call to the groundwater flow and transport model (GFTM) may take several hours on a reasonably fast workstation. For computational flexibility and efficiency, optimal groundwater remediation design at Lawrence Livermore National Laboratory (LLNL) has relied on artificial neural networks (ANNS) trained to approximate the outcome of 2-D field-scale, finite difference/finite element GFTMs. The search itself has been directed primarily by the genetic algorithm (GA) or the simulated annealing (SA) ... continued below

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12 p.; Other: FDE: PDF; PL:

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Rogers, L. L., LLNL May 1, 1998.

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One continuing point of research in optimizing groundwater quality management is reduction of computational burden which is particularly limiting in field-scale applications. Often evaluation of a single pumping strategy, i.e. one call to the groundwater flow and transport model (GFTM) may take several hours on a reasonably fast workstation. For computational flexibility and efficiency, optimal groundwater remediation design at Lawrence Livermore National Laboratory (LLNL) has relied on artificial neural networks (ANNS) trained to approximate the outcome of 2-D field-scale, finite difference/finite element GFTMs. The search itself has been directed primarily by the genetic algorithm (GA) or the simulated annealing (SA) algorithm. This approach has advantages of (1) up to a million fold increase in speed of remediation pattern assessment during the searches and sensitivity analyses for the 2-D LLNL work, (2) freedom from sequential runs of the GFTM (enables workstation farming), and (3) recycling of the knowledge base (i.e. runs of the GFTM necessary to train the ANNS). Reviewed here are the background and motivation for such work, recent applications, and continuing issues of research.

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12 p.; Other: FDE: PDF; PL:

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OSTI as DE98058715

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  • 1998 Berkeley initiative in soft computing-special interest group-earth sciences workshop, Berkeley, CA (United States), 3-6 Mar 1998

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  • Other: DE98058715
  • Report No.: UCRL-JC--129745
  • Report No.: CONF-9803107--
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 289658
  • Archival Resource Key: ark:/67531/metadc678380

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  • May 1, 1998

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  • July 25, 2015, 2:20 a.m.

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  • Feb. 16, 2016, 7:27 p.m.

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Rogers, L. L., LLNL. Groundwater remediation optimization using artificial neural networks, article, May 1, 1998; California. (digital.library.unt.edu/ark:/67531/metadc678380/: accessed August 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.