A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling

Helton, Jon C.; Davis, F.J.; Johnson, Jay

doi:10.1016/j.ress.2004.09.006

Cited by 237 publications

(114 citation statements)

References 115 publications

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“…44,55,56) In this analysis, the rank-transformed parameters were used instead of the raw parameter values, and the standardized rank regression coe cient (SRRC) was then calculated via regression analysis as a measure of variable importance. 57) Together with the stepwise regression analysis, statistical assumptions regarding the data used in the regression were veried in order to assure the reliability of the analysis. 25,51) e procedure involves 5 steps: a) Initial data screening to investigate the presence of multicollinearity which could greatly a ect the regression analysis results: 51) multicollinearity refers to a situation in which two or more predictor variables are highly inter-correlated with one another.…”

Section: Statistical Procedures For Uncertainty and Sensitivity Analysesmentioning

confidence: 99%

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

et al. 2012

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is paper demonstrates the procedures for probabilistic assessment of a pesticide fate and transport model, PCPF-1, to elucidate the modeling uncertainty using the Monte Carlo technique. Sensitivity analyses are performed to investigate the in uence of herbicide characteristics and related soil properties on model outputs using four popular rice herbicides: mefenacet, pretilachlor, bensulfuron-methyl and imazosulfuron. Uncertainty quanti cation showed that the simulated concentrations in paddy water varied more than those of paddy soil. is tendency decreased as the simulation proceeded to a later period but remained important for herbicides having either high solubility or a high 1st-order dissolution rate. e sensitivity analysis indicated that PCPF-1 parameters requiring careful determination are primarily those involve with herbicide adsorption (the organic carbon content, the bulk density and the volumetric saturated water content), secondary parameters related with herbicide mass distribution between paddy water and soil (1st-order desorption and dissolution rates) and lastly, those involving herbicide degradations.

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Section: Statistical Procedures For Uncertainty and Sensitivity Analysesmentioning

confidence: 99%

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

et al. 2012

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“…Under these conditions, Latin hypercube sampling is a very effective sampling strategy because its dense stratification across the range of each uncertain input results in a good representation of model behavior regardless of which predicted variable is under consideration and which elements of x actually affect the uncertainty in this variable. [124][125][126][127] Step 3. Propagate the sample generated in Step 2 through the model to obtain values for all model results of interest.…”

Section: Ccpf For Ymentioning

confidence: 99%

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory

Helton

Johnson²,

Oberkampf

et al. 2007

Computer Methods in Applied Mechanics and Engineering

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127

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Prepared by Sandia Nat ional Labor at ori es Albu querque , Ne w Mexico 87185 and Livermore , Ca liforn ia 94550Sandia is a mult iprogr am laborat or y operated by Sandia Corpo ratio n, a Lockh eed Mar tin Compa ny, for th e United States Depa rt ment of Energy's National Nuclear Securi ty Admi nistration under Contract DE-A C04-94AL 85000 .App rov ed for public release; furth er dissemination unlimited . (rI1) Sandia National LaboratoriesIssued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE:This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors.Printed in the United States of America. This report has been reproduced directly from the best available copy. AbstractEvidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a samplingbased computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impr...

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“…Therefore the addition of uncertainties in the input variables needs to be evaluated. Sensitivity analysis reflects the relationship between the uncertainty in the results and input variables [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Comparison Of Uncertainty Methods For Pipe Deflection Calculation

Wahanani¹

2016

KEn

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Reliability of pipe structure is one aspect to be considered in reactor safety analysis. MSC NASTRAN is a computer code that can be used to calculate pipe deflection for reliability evaluation. MSC PATRAN can be used to generate input for this code. Uncertainty evaluation needs to be done in the input variable to understand uncertainty range in the analysis results.A computer code for evaluating structure reliability has been developed in our previous study. The code has implemented latin hypercube sampling (LHS) to assess uncertainty in the input variable, such as load and modulus of elasticity. In this study, comparison of two uncertainty methods, i.e. simple random sampling (SRS) and LHS, was carried out for the developed software. The comparison was subjected to pipe deflection calculation using 100 samples. Comparison analysis shows that LHS method produces a robust mean of variance for all sample size. The results also confirm that variance of pipe deflection using LHS is smaller by 3% than SRS one. It can be concluded that LHS is appropriate to be implemented for uncertainty analysis in the developed code.

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A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling

Cited by 237 publications

References 115 publications

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory

Comparison Of Uncertainty Methods For Pipe Deflection Calculation

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