Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis - Tapa blanda

Doherty, John E; Hunt, Randall J; Tonkin, Matthew J

 
9781500299989: Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis

Sinopsis

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncer- tainty analysis with regard to models can be very computa- tionally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system proper- ties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints).

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Reseña del editor

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncer- tainty analysis with regard to models can be very computa- tionally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system proper- ties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints).

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Otras ediciones populares con el mismo título

9781243018922: Approaches to Highly Parameterized Inversion: A Guide to Using Pest for Model-Parameter and Predictive-Uncertainty Analysis: Usgs Scientific Investiga

Edición Destacada

ISBN 10:  1243018925 ISBN 13:  9781243018922
Editorial: BiblioGov, 2011
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