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A review of techniques for parameter sensitivity analysis of environmental models

A review of techniques for parameter sensitivity analysis of environmental models Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a ‘sensitivity analysis’. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Monitoring and Assessment Springer Journals

A review of techniques for parameter sensitivity analysis of environmental models

Environmental Monitoring and Assessment , Volume 32 (2) – Oct 13, 2004

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References (72)

Publisher
Springer Journals
Copyright
Copyright
Subject
Environment; Monitoring/Environmental Analysis; Environmental Management; Ecotoxicology; Atmospheric Protection/Air Quality Control/Air Pollution; Ecology
ISSN
0167-6369
eISSN
1573-2959
DOI
10.1007/BF00547132
Publisher site
See Article on Publisher Site

Abstract

Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a ‘sensitivity analysis’. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.

Journal

Environmental Monitoring and AssessmentSpringer Journals

Published: Oct 13, 2004

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