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Chapter
Apr 26, 2012

Parameter Estimation Using an Artificial Neural Network to Incorporate Multiple Types of Data

Publication: World Environmental and Water Resource Congress 2006: Examining the Confluence of Environmental and Water Concerns

Abstract

We apply a modified counterpropagation artificial neural network (ANN) that uses multivariate data to several parameter estimation problems: (1) estimation of small scale Berea sandstone geophysical properties, (2) estimation of apparent conductivity at a leaking landfill using electromagnetic data and (3) estimation of hydraulic conductivity field at a landfill in New York State using pumping test and well log data. The counterpropagation algorithm has been enhanced in this research to allow for spatial interpolation that is comparable to traditional kriging methods. This enhanced ANN is data-driven, can incorporate large amounts of multiple data types to produce parameter estimates in real-time and does not require the computation of large covariance matrices associated with traditional geostatistical methods (kriging).

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Go to World Environmental and Water Resource Congress 2006
World Environmental and Water Resource Congress 2006: Examining the Confluence of Environmental and Water Concerns
Pages: 1 - 7

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Published online: Apr 26, 2012

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L. E. Besaw [email protected]
University of Vermont, Department of Civil and Environmental Engineering, 33 Colchester Ave, 213 Votey Building, Burlington, VT 05405;. E-mail: [email protected]
Donna M. Rizzo [email protected]
University of Vermont, Department of Civil and Environmental Engineering, 33 Colchester Ave, 213 Votey Building, Burlington, VT 05405;. E-mail: [email protected]
Paula J. Mouser [email protected]
University of Vermont, Department of Civil and Environmental Engineering, 33 Colchester Ave, 213 Votey Building, Burlington, VT 05405;. E-mail: [email protected]

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