Groundwater remediation optimization using artificial neural networks
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.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 289658
- Report Number(s):
- UCRL-JC-129745; CONF-9803107-; ON: DE98058715
- Resource Relation:
- Conference: 1998 Berkeley initiative in soft computing-special interest group-earth sciences workshop, Berkeley, CA (United States), 3-6 Mar 1998; Other Information: PBD: 1 May 1998
- Country of Publication:
- United States
- Language:
- English
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