A New Framework for Adptive Sampling and Analysis During Long-Term Monitoring and Remedial Action Management

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Yonas Demissie, a research assistant supported by the project, has successfully created artificial data and assimilated it into coupled Modflow and artificial neural network models. His initial findings show that the neural networks help correct errors in the Modflow models. Abhishek Singh has used test cases from the literature to show that performing model calibration with an interactive genetic algorithm results in significantly improved parameter values. Meghna Babbar, the third research assistant supported by the project, has found similar results when applying an interactive genetic algorithms to long-term monitoring design. She has also developed new types of interactive genetic algorithms ... continued below

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Minsker, Barbara June 1, 2005.

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Description

Yonas Demissie, a research assistant supported by the project, has successfully created artificial data and assimilated it into coupled Modflow and artificial neural network models. His initial findings show that the neural networks help correct errors in the Modflow models. Abhishek Singh has used test cases from the literature to show that performing model calibration with an interactive genetic algorithm results in significantly improved parameter values. Meghna Babbar, the third research assistant supported by the project, has found similar results when applying an interactive genetic algorithms to long-term monitoring design. She has also developed new types of interactive genetic algorithms that significantly improve performance. Gayathri Gopalakrishnan, the last research assistant who is partially supported by the project, has shown that sampling branches of phytoremediation trees is an accurate approach to estimating soil and groundwater contaminations in areas surrounding the trees at the Argonne 317/319 site.

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  • Report No.: EMSP-87023B-2005
  • Grant Number: None
  • DOI: 10.2172/894014 | External Link
  • Office of Scientific & Technical Information Report Number: 894014
  • Archival Resource Key: ark:/67531/metadc882356

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  • June 1, 2005

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Nov. 4, 2016, 3:33 p.m.

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Minsker, Barbara. A New Framework for Adptive Sampling and Analysis During Long-Term Monitoring and Remedial Action Management, report, June 1, 2005; United States. (digital.library.unt.edu/ark:/67531/metadc882356/: accessed September 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.