Chapter
Apr 26, 2012
Parameter Estimation Using an Artificial Neural Network to Incorporate Multiple Types of Data
Authors: L. E. Besaw [email protected], Donna M. Rizzo [email protected], and Paula J. Mouser [email protected]Author Affiliations
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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© 2006 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer models
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Environmental engineering
- Field tests
- Hydraulic engineering
- Hydraulic properties
- Hydrologic data
- Hydrologic engineering
- Hydrology
- Kriging
- Landfills
- Mathematics
- Models (by type)
- Neural networks
- Parameters (statistics)
- Pumping tests
- Statistics
- Tests (by type)
- Waste management
- Waste sites
- Water and water resources
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Affiliations
University of Vermont, Department of Civil and Environmental Engineering, 33 Colchester Ave, 213 Votey Building, Burlington, VT 05405;. E-mail: [email protected]
University of Vermont, Department of Civil and Environmental Engineering, 33 Colchester Ave, 213 Votey Building, Burlington, VT 05405;. E-mail: [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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