Sensitivity analysis of model output

Chan, Karen; Saltelli, Andrea; Tarantola, Stefano

doi:10.1145/268437.268489

Cited by 178 publications

(15 citation statements)

References 16 publications

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“…The main difference between FAST and Sobol's method is the underlying algorithm in multidimensional integration of the sensitivity indices. While the pattern search uses a sinusoidal function in FAST method, a Monte Carlo integration method is used in Sobol sensitivity analysis …”

Section: Systems Pharmacology Modelsmentioning

confidence: 99%

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

et al. 2015

CPT Pharmacom & Syst Pharma

285

218

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A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015

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Section: Systems Pharmacology Modelsmentioning

confidence: 99%

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

et al. 2015

CPT Pharmacom & Syst Pharma

285

218

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“…To assess the sensitivity of modelled streamflow and GPP to the parameters of the SDGVM, we perform a sensitivity analysis using a Bayesian approach. The analysis produces the percentage of variance explained by each parameter (also called the correlation ratio (Chan et al , 1997), importance measure or sensitivity index). It is defined as where Y is the output variable (GPP or streamflow) and X i is the i th model parameter.…”

Section: Methodsmentioning

confidence: 99%

“…The variance calculations for S i require a large number of model evaluations by Monte Carlo (MC) computation (Chan et al , 1997), which can be impractical for complex models because of limited CPU resources. Using the emulator technique (Sacks et al , 1989; Kennedy & O'Hagan, 2001; Oakley & O'Hagan, 2004; O'Hagan, 2004), the number of model evaluations is cut down dramatically, without losing accuracy.…”

Section: Methodsmentioning

confidence: 99%

Constraining the Sheffield dynamic global vegetation model using stream‐flow measurements in the United Kingdom

Picard

Woodward

Lomas

et al. 2005

Global Change Biology

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The biospheric water and carbon cycles are intimately coupled, so simulating carbon fluxes by vegetation also requires modelling of the water fluxes, with each component influencing the other. Observations of river streamflow integrate information at the catchment scale and are widely available over a long period; they therefore provide an important source of information for validating or calibrating vegetation models. In this paper, we analyse the performance of the Sheffield dynamic global vegetation model (SDGVM) for predicting river streamflow and quantifying how this information helps to constrain carbon flux predictions.The SDGVM is run for 29 large catchments in the United Kingdom. Annual streamflow estimates are compared with long time-series observations. In 23 out of the 29 catchments, the bias between model and observations is less than 50 mm, equivalent to less than 10% of precipitation. In the remaining catchments, larger errors are because of combinations of unpredictable causes, in particular various human activities and measurement issues and, in two cases, unidentified causes.In one of the catchments, we assess to what extent a knowledge of annual streamflow can constrain model parameters and in turn constrain estimates of gross primary production (GPP). For this purpose, we assume the model parameters are uncertain and constrain them by the streamflow observations using the generalized likelihood uncertainty estimation method. Comparing the probability density function of GPP with and without constraint shows that streamflow effectively constrains GPP, mainly by setting a low probability to GPP values below about 1100 g C À1 m 2 yr À1 . In other words, streamflow observations allow the rejection of low values of GPP, so that the potential range of possible GPP values is almost halved.

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“…The S i∈{1,2,...,n} are called first-order Sobol' indices or sensitivity indices [2,9]. The total effect index [1] includes interaction effects in addition to the first-order sensitivity indices, and can be defined as…”

Section: Sobol' Indices and High-dimensional Model Representation (Hdmr)mentioning

confidence: 99%

Variance-based interaction index measuring heteroscedasticity

Ito

Couckuyt

Poles

et al. 2016

Computer Physics Communications

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This work is motivated by the need to deal with models with high-dimensional input spaces of real variables. One way to tackle high-dimensional problems is to identify interaction or non-interaction among input parameters. We propose a new variance-based sensitivity interaction index that can detect and quantify interactions among the input variables of mathematical functions and computer simulations. The computation is very similar to first-order sensitivity indices by Sobol'. The proposed interaction index can quantify the relative importance of input variables in interaction. Furthermore, detection of non-interaction for screening can be done with as low as 4n + 2 function evaluations, where n is the number of input variables. Using the interaction indices based on heteroscedasticity, the original function may be decomposed into a set of lower dimensional functions which may then be analyzed separately.

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Sensitivity analysis of model output

Cited by 178 publications

References 16 publications

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Constraining the Sheffield dynamic global vegetation model using stream‐flow measurements in the United Kingdom

Variance-based interaction index measuring heteroscedasticity

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