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ARS Home » Research » Publications at this Location » Publication #152912
Title: MODEL ABSTRACTION TECHNIQUES RELATED TO PARAMETER ESTIMATION AND UNCERTAINTY

Author
item Pachepsky, Yakov
item Van Genuchten, Martinus
item NICHOLSON, THOMAS - US NUCLEAR REG. COM.
item CADY, RALPH - US NUCLEAR REG. COM.

Submitted to: International Workshop on Uncertainty, Sensitivity, and Parameter Estimation For Multimedia Environmental Modeling
Publication Type: Abstract Only
Publication Acceptance Date: 8/20/2003
Publication Date: 8/21/2003
Citation: Pachepsky, Y.A., Van Genuchten, M.T., Nicholson, T., Cady, R. 2003. Model abstraction techniques related to parameter estimation and uncertainty. International Workshop on Uncertainty, Sensitivity, and Parameter Estimation For Multimedia Environmental Modeling. p.2.

Interpretive Summary:

Technical Abstract: Model abstraction is a methodology for reducing the complexity of a simulation model while maintaining the validity of the simulation results with respect to the question that the simulation is being used to address. The need for model abstraction is recognised in simulations of complex systems where increased level of detail does not necessarily imply increased accuracy of simulation results, but usually increases computational complexity, data collection density, uncertainty of results, and may make simulation results more difficult to interpret. Simplifications that result from appropriate model abstractions may make the description of the problem more easily relayed to and understandable by others, including decision-makers and the public. It is often imperative to explicitly acknowledge the abstraction strategy used and its inherent biases, so that the modeling process is transparent and tractable. Model abstraction explicitly deals with uncertainties in model structure. We review model abstraction techniques and examples of their application in subsurface flow and transport. We conclude that for model development calibration, and simulations, prospective direction should be model structure, model function, and model form modifications, respectively. Field data sets for a humid and for an arid environment were selected based on their completeness and complexity to explore specific issues. Future work with field data sets will compare the efficiency of model analysis techniques and provide a basis for developing rule-based strategies for model abstraction in the area of subsurface water and solute transport.