Journal Article10.1016/J.ENVSOFT.2006.10.004
An effective screening design for sensitivity analysis of large models
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TL;DR: A revised version of the elementary effects method is proposed, improved in terms of both the definition of the measure and the sampling strategy, having the advantage of a lower computational cost.
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Abstract: In 1991 Morris proposed an effective screening sensitivity measure to identify the few important factors in models with many factors. The method is based on computing for each input a number of incremental ratios, namely elementary effects, which are then averaged to assess the overall importance of the input. Despite its value, the method is still rarely used and instead local analyses varying one factor at a time around a baseline point are usually employed. In this piece of work we propose a revised version of the elementary effects method, improved in terms of both the definition of the measure and the sampling strategy. In the present form the method shares many of the positive qualities of the variance-based techniques, having the advantage of a lower computational cost, as demonstrated by the analytical examples. The method is employed to assess the sensitivity of a chemical reaction model for dimethylsulphide (DMS), a gas involved in climate change. Results of the sensitivity analysis open up the ground for model reconsideration: some model components may need a more thorough modelling effort while some others may need to be simplified.
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Citations
Global and local sensitivity analysis to improve the understanding of physically-based urban wash-off models from high-resolution laboratory experiments.
TL;DR: Physically-based urban wash-off models are a promising means of studying the transport of finer suspended solids and their associated pollutants during rain events, considering spatial and temporal heterogeneities through an in-depth sensitivity analysis.
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A framework to use crop models for multi-objective constrained optimization of irrigation strategies
Bruno Cheviron,R. Willem Vervoort,Rami Albasha,Romain Dairon,Camille Le Priol,Jean-Claude Mailhol +5 more
TL;DR: An innovative framework to use crop models which combines sensitivity analysis, uncertainty analysis and constrained optimisation runs for irrigation optimisation purposes, facing competing constraints on several agricultural variables is discussed.
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US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy
TL;DR: This model-based analysis suggests that market forces are likely to keep US energy-related greenhouse gas emissions relatively flat or produce modest reductions in the absence of new federal policy.
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Efficient screening of climate model sensitivity to a large number of perturbed input parameters
TL;DR: The Morris one-at-a-time (MOAT) method as mentioned in this paper is a parameter sensitivity screening algorithm for climate modeling, which can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination.
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References
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TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.
•Journal Article
Factorial sampling plans for preliminary computational experiments
TL;DR: The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input.
4.4K
Design and Analysis of Computer Experiments
Magnus Arnér
- 07 Mar 2014
TL;DR: It is concluded that the strategy as proposed by Sacks and coworkers is not suited for implementation in a design optimization tool, mainly because of two reasons: maximum likelihood parameter estimation is computationally expensive and not straightforward, while the quality of the parameter estimations is questionable.
Factorial sampling plans for preliminary computational experiments
TL;DR: In this article, the problem of designing computational experiments to determine which inputs have important effects on an output is considered, and experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects.
2.5K