Sensitivity Analysis in Practice

Saltelli, Andrea; Tarantola, Stefano; Campolongo, Francesca; Ratto, Marco

doi:10.1002/0470870958

Cited by 684 publications

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“…Global SA, on the other hand, studies the effects of input variations on model outputs in the entire allowable ranges of the input space. Saltelli et al [7,9] have defined global SA methods by two properties:…”

Section: Introductionmentioning

confidence: 99%

Self-validated variance-based methods for sensitivity analysis of model outputs

Tong

2010

Reliability Engineering & System Safety

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Global sensitivity analysis (GSA) has the advantage over local sensitivity analysis in that GSA does not require strong model assumptions such as linearity or monotonicity. As a result, GSA methods such as those based on variance decomposition are well-suited to multi-physics models, which are often plagued by large nonlinearities. However, as with many other sampling-based methods, inadequate sample size can badly pollute the result accuracies. A natural remedy is to adaptively increase the sample size until sufficient accuracy is obtained. This paper proposes an iterative methodology comprising mechanisms for guiding sample size selection and self-assessing result accuracy. The elegant features in the the proposed methodology are the adaptive refinement strategies for stratified designs. We first apply this iterative methodology to the design of a self-validated first-order sensitivity analysis algorithm. We also extend this methodology to design a self-validated second-order sensitivity analysis algorithm based on refining replicated orthogonal array designs. Several numerical experiments are given to demonstrate the effectiveness of these methods.

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Section: Introductionmentioning

confidence: 99%

Self-validated variance-based methods for sensitivity analysis of model outputs

Tong

2010

Reliability Engineering & System Safety

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“…Sensitivity analysis results are dependent on the chosen method (Saltelli et al, 2004(Saltelli et al, , 2000. In general, quantitative values of different sensitivity measures may vary.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Uncertainty and sensitivity in a bank stability model: implications for estimating phosphorus loading

Lammers

Bledsoe

Langendoen

2016

Earth Surf Processes Landf

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UNCERTAINTY AND SENSITIVITY IN A BANK STABILITY MODEL: IMPLICATIONS FOR ESTIMATING PHOSPHORUS LOADINGEutrophication of aquatic ecosystems is one of the most pressing water quality concerns in the U.S. and around the world. Bank erosion has been largely overlooked as a source of nutrient loading, despite field studies demonstrating that this source can account for the majority of the total phosphorus budget of a watershed. Substantial effort has been made to develop mechanistic models to predict bank erosion and instability in stream systems; however, these models do not account for inherent natural variability in input values. Providing only single output values with no quantification of associated uncertainty can complicate management decisions focused on reducing bank erosion and nutrient loading to streams. To address this issue, uncertainty and sensitivity analyses were performed on the Bank Stability and Toe Erosion Model (BSTEM), a mechanistic model developed by the USDA-ARS that simulates both mass wasting (stability) and fluvial erosion of streambanks. Sensitivity analysis results indicate that variable influence on model output can vary depending on assumed input distributions.Generally, bank height, soil cohesion, and plant species were found to be most influential in determining stability of clay (cohesive) banks. In addition to these three inputs, groundwater elevation, stream stage, and bank angle were also identified as important in sand (non-cohesive) banks. Slope and bank height are the dominant variables in fluvial erosion modeling, while erodibility and critical shear stress are relatively unimportant. However, the threshold effect of critical shear stress (determining whether erosion occurs) was not explicitly accounted for, ii possibly explaining the relatively low sensitivity indices for this variable. Model output distributions of sediment and phosphorus loading rates corresponded well to ranges published in the literature, helping validate both model performance and selected ranges of input values. In addition, a probabilistic modeling approach was applied to data from a watershed-scale sediment and phosphorus loading study on the Missisquoi River, Vermont to quantify uncertainty associated with these published results. While our estimates indicated that bank erosion was likely a significant source of sediment and phosphorus to the watershed in question, the uncertainty associated with these predictions indicates that they should probably be considered order of magnitude estimates only.iii ACKNOWLEDGEMENTS

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“…This 9 The toolbox is available online at http://eemc.jrc.ec.europa. eu/Software-DYNARE.htm, the tools are described in Saltelli et al (2004 and2008) and Ratto (2008 and2009). Table 1 Restrictions imposed on parameters in model variants Table 2 and Table 3 describes, which parameters (and in which direction) influence the Cumulative probability density functions shifted to the left off the dashed line in first two panels correspond to the observation in Table 2 that lower ranges of H θ and F θ are responsible for unstable results.…”

Section: Stability Analysismentioning

confidence: 99%

Comparing the fit of New Keynesian DSGE models

Čapek¹

2010

ER-CEREI

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The paper is focused on an analysis of model fit of Dynamic Stochastic General Equilibrium (DSGE) models following New Open Economy Macroeconomics (NOEM). Unlike most of the literature on the topic, this paper does not use Bayesian posterior odds ratio to analyze model fit to data; it uses alternative tools instead. In order to compare the results of the alternative tools to the standard posterior odds ratio, this paper uses the findings of Slanicay and Vašíček (2009), who compared model fit to data of several models with the tool Bayesian posterior odds ratio. The goal of the paper is to verify the results of Slanicay and Vašíček's (2009) model variants with different criteria than posterior odds and to compare the results with findings of their paper. The tools for the analysis are criteria based on root mean squared error (RMSE) and tools from the Global Sensitivity Analysis toolbox. Conclusions of this paper are the following: Habit persistence in consumption is found to be important and price indexation unimportant as in Slanicay and Vašíček (2009). Furthermore, model variants with foreign economy modeled as AR1 processes always perform better than the ones with structurally modeled foreign economy. This finding is in contradiction to the results of Slanicay and Vašíček (2009).

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Sensitivity Analysis in Practice

Cited by 684 publications

References 0 publications

Self-validated variance-based methods for sensitivity analysis of model outputs

Self-validated variance-based methods for sensitivity analysis of model outputs

Uncertainty and sensitivity in a bank stability model: implications for estimating phosphorus loading

Comparing the fit of New Keynesian DSGE models

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