Sensitivity analysis using probability bounding

Ferson, Scott; Tucker, Warwick

doi:10.1016/j.ress.2005.11.052

Cited by 101 publications

(57 citation statements)

References 14 publications

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“…The approach, based on the pinching method of Ferson and Tucker (2006), is implemented using the PINCHING_fun and SENSI_PINCHING functions. In this section, we present the study used to describe and illustrate the functionalities of the HYRISK package.…”

Section: Package Functionalitiesmentioning

confidence: 99%

“…In this section, we present the study used to describe and illustrate the functionalities of the HYRISK package. This case study is focused on the stability analysis of a dike as described by Ferson and Tucker (2006). The dike has revetments made of masonry blocks subject to wave action as depicted schematically in Fig.…”

Section: Package Functionalitiesmentioning

confidence: 99%

“…When mixed representations of uncertainty are present, a practical method for sensitivity analysis is the pinching approach proposed by Ferson and Tucker (2006) assessing how the imprecision on the output would reduce if additional information on the input parameters were available, i.e. if imprecision in the inputs were removed / reduced.…”

Section: Step 3: Sensitivitymentioning

confidence: 99%

“…In a first section, we summarise the main choices for the package design in order to fill the gap identified among the available open-source software solutions for uncertainty quantification with mixed uncertainty representation tools. Then, we describe the case study of a dike stability analysis (adapted from (Ferson and Tucker 2006)), which is used to describe and illustrate the HYRISK functionalities for the uncertainty quantification procedure in the subsequent sections.…”

Section: Introductionmentioning

confidence: 99%

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HYRISK: An R package for hybrid uncertainty analysis using probability, imprecise probability and possibility distributions

Rohmer¹,

manceau²,

Guyonnet³

et al. 2018

Preprint

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Uncertainty analysis is an unavoidable risk assessment task (for instance for natural hazards, or for environmental issues). In situations where data are scarce, incomplete or imprecise, the systematic and only use of probabilities can be debatable. Over the last years, several alternative mathematical representation methods have been developed to handle in a more flexible manner the lack of knowledge related to input parameters of risk assessment models. This article presents an R package HYRISK dedicated to jointly handling different mathematical representation tools, namely probabilities, possibility distributions and probability functions with imprecise parameters, for the different stages of uncertainty treatment in risk assessments (i.e. uncertainty representation, propagation, sensitivity analysis and decision-making). We support the description using the case study of a dike stability analysis. The package is available at: https://cran.r-project.org/web/packages/HYRISK/index.html.

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Section: Package Functionalitiesmentioning

confidence: 99%

Section: Package Functionalitiesmentioning

confidence: 99%

Section: Step 3: Sensitivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HYRISK: An R package for hybrid uncertainty analysis using probability, imprecise probability and possibility distributions

Rohmer¹,

manceau²,

Guyonnet³

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…However, all these require specific probability distribution in modeling both model parameters and model structure. In the literature, previous non-probabilistic methods of sensitivity analysis are developed [16,17]. Several studies have confirmed the robustness of use of Hartley-like measure to apply sensitivity analysis in fuzzy theory framework in numerous fields [33][34][35].…”

Section: Introductionmentioning

confidence: 97%

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

Ferchichi

Boulila

Farah

2016

Vietnam J Comput Sci

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This paper presents an approach for reducing uncertainty related to the process of land-cover change (LCC) prediction. LCC prediction models have, almost, two sources of uncertainty which are the uncertainty related to model parameters and the uncertainty related to model structure. These uncertainties have a big impact on decisions of the prediction model. To deal with these problems, the proposed approach is divided into three main steps: (1) an uncertainty propagation step based on possibility theory is used as a tool to evaluate the performance of the model; (2) a sensitivity analysis step based on Hartley-like measure is then used to find the most important sources of uncertainty; and (3) a knowledge base based on machine learning algorithm is built to identify the reduction factors of all uncertainty sources of parameters and to reshape their values to reduce in a significant way the uncertainty about future changes of land cover. In this study, the present and future growths of two case studies were anticipated using multi-temporal Spot-4 and Landsat satellite images. These data are used for the preparation of prediction map of year 2025. The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling. B Ahlem Ferchichi

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