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Title: Optimal groundwater remediation using artificial neural networks and the genetic algorithm

Abstract

An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

Authors:
 [1]
  1. Stanford Univ., CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
10102700
Report Number(s):
UCRL-LR-114125
ON: DE93040855; TRN: 94:003420
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Thesis/Dissertation
Resource Relation:
Other Information: TH: Thesis (Ph.D.); PBD: Aug 1992
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; GROUND WATER; REMEDIAL ACTION; ALGORITHMS; NEURAL NETWORKS; ENVIRONMENTAL TRANSPORT; ARTIFICIAL INTELLIGENCE; 540220; 990200; CHEMICALS MONITORING AND TRANSPORT; MATHEMATICS AND COMPUTERS

Citation Formats

Rogers, Leah L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. United States: N. p., 1992. Web. doi:10.2172/10102700.
Rogers, Leah L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. United States. https://doi.org/10.2172/10102700
Rogers, Leah L. 1992. "Optimal groundwater remediation using artificial neural networks and the genetic algorithm". United States. https://doi.org/10.2172/10102700. https://www.osti.gov/servlets/purl/10102700.
@article{osti_10102700,
title = {Optimal groundwater remediation using artificial neural networks and the genetic algorithm},
author = {Rogers, Leah L.},
abstractNote = {An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.},
doi = {10.2172/10102700},
url = {https://www.osti.gov/biblio/10102700}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Aug 01 00:00:00 EDT 1992},
month = {Sat Aug 01 00:00:00 EDT 1992}
}

Thesis/Dissertation:
Other availability
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