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Guyonnet D, Coftier A, Bataillard P, Destercke S. Risk-based imprecise post-remediation soil quality objectives. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 923:171445. [PMID: 38442757 DOI: 10.1016/j.scitotenv.2024.171445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/22/2024] [Accepted: 03/01/2024] [Indexed: 03/07/2024]
Abstract
While risk-based contaminated land management is an essential component of sustainable remediation, uncertainty is an unavoidable aspect of risk assessment, since most of the parameters that influence risk are typically affected by uncertainty. Uncertainty may be of different origins; i.e., stochastic or epistemic. Stochastic (or aleatoric) uncertainty arises from random variability related to natural processes, while epistemic uncertainty arises from the incomplete/imprecise nature of available information. But the latter is rarely considered in risk assessments, with the result that risk-based soil quality objectives are almost invariably presented as precise (unique) threshold values. In this paper it is shown: (i) how the joint treatment of stochastic and epistemic uncertainty in risk assessment can lead to soil quality objectives presented as intervals rather than precise values and (ii) how this provides an upper risk-based safeguard for post-remediation monitoring values. The proposed method is illustrated by a real case of soils contaminated by arsenic located in the North-East of France. At this site steel manufacturers have gradually filled up a small valley with slag and dust, over more than a century. These materials are enriched in various metal(loid)s, including arsenic and lead. As the environmental authority has asked for a conversion of the site to other uses that may involve access by the general public, an investigation of human health risk was performed based on a sampling campaign and chemical characterizations including various types of extractions and an analysis of bioaccessibility. While further investigations are required to improve the bioaccessibility model, the human health risk presented herein shows how partial or imprecise information can be incorporated in the analysis while taking into account underlying uncertainties.
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Dutta P, Akhtari S. A General Amalgamate Technique to Evaluate Human Health Risk Under Uncertain Circumstances. JOURNAL OF HEALTH MANAGEMENT 2022. [DOI: 10.1177/09720634211072596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Evaluation of humans’ health risk is an essential and most demanding aid in relevance to the process of decision-making. Accumulation of quality knowledge on the attributes of each and every available data, information and model parameters, involved in risk assessment, plays a crucial role in the process of evaluation. It is important to note that, most frequently, model parameters are imprecise due to the availability of limited data and knowledge. Under such circumstances, probability theory (PT) and the theory of fuzzy sets can be brought forth to deal with the emerging uncertainties. There is also a need to devise an amalgamate technique to perform health risk assessment under uncertainty. Although some different approaches are available in this regard, all approaches are situation or problem dependent and fail to address some specific issues. Therefore, this article presents a general amalgamate technique to address all the concerned issues, and, finally, health risk is carried out using this approach.
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Affiliation(s)
- Palash Dutta
- Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India
| | - Sayesta Akhtari
- Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, India
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Duran-Vinuesa L, Cuervo D. Uncertainty quantification and propagation with probability boxes. NUCLEAR ENGINEERING AND TECHNOLOGY 2021. [DOI: 10.1016/j.net.2021.02.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Belna M, Ndiaye A, Taillandier F, Agabriel L, Marie AL, Gésan-Guiziou G. Formulating multiobjective optimization of 0.1 μm microfiltration of skim milk. FOOD AND BIOPRODUCTS PROCESSING 2020. [DOI: 10.1016/j.fbp.2020.09.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation. Processes (Basel) 2019. [DOI: 10.3390/pr7080509] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.
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Tran TA, Jauberthie C, Le Gall F, Travé-Massuyès L. Evidential box particle filter using belief function theory. Int J Approx Reason 2018. [DOI: 10.1016/j.ijar.2017.10.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Jiang W, Zhuang M, Xie C. A Reliability-Based Method to Sensor Data Fusion. SENSORS 2017; 17:s17071575. [PMID: 28678179 PMCID: PMC5539540 DOI: 10.3390/s17071575] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2017] [Revised: 07/01/2017] [Accepted: 07/03/2017] [Indexed: 11/16/2022]
Abstract
Multi-sensor data fusion technology based on Dempster–Shafer evidence theory is widely applied in many fields. However, how to determine basic belief assignment (BBA) is still an open issue. The existing BBA methods pay more attention to the uncertainty of information, but do not simultaneously consider the reliability of information sources. Real-world information is not only uncertain, but also partially reliable. Thus, uncertainty and partial reliability are strongly associated with each other. To take into account this fact, a new method to represent BBAs along with their associated reliabilities is proposed in this paper, which is named reliability-based BBA. Several examples are carried out to show the validity of the proposed method.
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Affiliation(s)
- Wen Jiang
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Miaoyan Zhuang
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Chunhe Xie
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
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Pedroni N, Zio E, Pasanisi A, Couplet M. A Critical Discussion and Practical Recommendations on Some Issues Relevant to the Nonprobabilistic Treatment of Uncertainty in Engineering Risk Assessment. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2017; 37:1315-1340. [PMID: 28095591 DOI: 10.1111/risa.12705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 04/26/2016] [Accepted: 07/13/2016] [Indexed: 06/06/2023]
Abstract
Models for the assessment of the risk of complex engineering systems are affected by uncertainties due to the randomness of several phenomena involved and the incomplete knowledge about some of the characteristics of the system. The objective of this article is to provide operative guidelines to handle some conceptual and technical issues related to the treatment of uncertainty in risk assessment for engineering practice. In particular, the following issues are addressed: (1) quantitative modeling and representation of uncertainty coherently with the information available on the system of interest; (2) propagation of the uncertainty from the input(s) to the output(s) of the system model; (3) (Bayesian) updating as new information on the system becomes available; and (4) modeling and representation of dependences among the input variables and parameters of the system model. Different approaches and methods are recommended for efficiently tackling each of issues (1)-(4) above; the tools considered are derived from both classical probability theory as well as alternative, nonfully probabilistic uncertainty representation frameworks (e.g., possibility theory). The recommendations drawn are supported by the results obtained in illustrative applications of literature.
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Affiliation(s)
- Nicola Pedroni
- Chair "System Science and the Energy Challenge"-Fondation Electricité de France (EdF) at the Laboratoire Genie Industriel (LGI), CentraleSupélec, Université Paris-Saclay, Chatenay-Malabry, France
| | - Enrico Zio
- Chair "System Science and the Energy Challenge"-Fondation Electricité de France (EdF) at the Laboratoire Genie Industriel (LGI), CentraleSupélec, Université Paris-Saclay, Chatenay-Malabry, France
- Energy Department, Politecnico di Milano, Milano, Italy
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José Palacios J, González-Rodríguez I, Vela CR, Puente J. Robust multiobjective optimisation for fuzzy job shop problems. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.07.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Sensing Attribute Weights: A Novel Basic Belief Assignment Method. SENSORS 2017; 17:s17040721. [PMID: 28358325 PMCID: PMC5421681 DOI: 10.3390/s17040721] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 03/25/2017] [Accepted: 03/27/2017] [Indexed: 02/04/2023]
Abstract
Dempster-Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an open issue. The existing methods to determine BBA do not consider the reliability of each attribute; at the same time, they cannot effectively determine BBA in the open world. In this paper, based on attribute weights, a novel method to determine BBA is proposed not only in the closed world, but also in the open world. The Gaussian model of each attribute is built using the training samples firstly. Second, the similarity between the test sample and the attribute model is measured based on the Gaussian membership functions. Then, the attribute weights are generated using the overlap degree among the classes. Finally, BBA is determined according to the sensed attribute weights. Several examples with small datasets show the validity of the proposed method.
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Dubois D, Prade H. Practical Methods for Constructing Possibility Distributions. INT J INTELL SYST 2015. [DOI: 10.1002/int.21782] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Didier Dubois
- IRIT; CNRS and Université de Toulouse; 31062 Toulouse Cedex 09 France
| | - Henri Prade
- IRIT; CNRS and Université de Toulouse; 31062 Toulouse Cedex 09 France
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LO CHUNGKUNG, PEDRONI N, ZIO E. TREATING UNCERTAINTIES IN A NUCLEAR SEISMIC PROBABILISTIC RISK ASSESSMENT BY MEANS OF THE DEMPSTER-SHAFER THEORY OF EVIDENCE. NUCLEAR ENGINEERING AND TECHNOLOGY 2014. [DOI: 10.5516/net.03.2014.701] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Aliev R, Pedrycz W, Zeinalova L, Huseynov O. Decision Making with Second-Order Imprecise Probabilities. INT J INTELL SYST 2013. [DOI: 10.1002/int.21630] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Rafik Aliev
- Department of Master of Business Administration; Azerbaijan State Oil Academy; Baku AZ1010 Azerbaijan
| | - W. Pedrycz
- Department of Electrical and Computer Engineering; University of Alberta; Edmonton AB T6R 2G7 Canada
- System Research Institute; Polish Academy of Sciences; Warsaw Poland
| | - L.M. Zeinalova
- Department of Computer-Aided Control Systems; Azerbaijan State Oil Academy; Baku AZ1010 Azerbaijan
| | - O.H. Huseynov
- Department of Computer-Aided Control Systems; Azerbaijan State Oil Academy; Baku AZ1010 Azerbaijan
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Pedroni N, Zio E. Uncertainty analysis in fault tree models with dependent basic events. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2013; 33:1146-1173. [PMID: 23078089 DOI: 10.1111/j.1539-6924.2012.01903.x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In general, two types of dependence need to be considered when estimating the probability of the top event (TE) of a fault tree (FT): "objective" dependence between the (random) occurrences of different basic events (BEs) in the FT and "state-of-knowledge" (epistemic) dependence between estimates of the epistemically uncertain probabilities of some BEs of the FT model. In this article, we study the effects on the TE probability of objective and epistemic dependences. The well-known Frèchet bounds and the distribution envelope determination (DEnv) method are used to model all kinds of (possibly unknown) objective and epistemic dependences, respectively. For exemplification, the analyses are carried out on a FT with six BEs. Results show that both types of dependence significantly affect the TE probability; however, the effects of epistemic dependence are likely to be overwhelmed by those of objective dependence (if present).
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Affiliation(s)
- Nicola Pedroni
- Energy Department, Politecnico di Milano, Via Ponzio 34/3, 20133 Milano, Italy
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Troffaes MC, Miranda E, Destercke S. On the connection between probability boxes and possibility measures. Inf Sci (N Y) 2013. [DOI: 10.1016/j.ins.2012.09.033] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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PEDRONI NICOLA, ZIO ENRICO. EMPIRICAL COMPARISON OF METHODS FOR THE HIERARCHICAL PROPAGATION OF HYBRID UNCERTAINTY IN RISK ASSESSMENT, IN PRESENCE OF DEPENDENCES. INT J UNCERTAIN FUZZ 2012. [DOI: 10.1142/s0218488512500250] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Risk analysis models describing aleatory (i.e., random) events contain parameters (e.g., probabilities, failure rates, …) that are epistemically-uncertain, i.e., known with poor precision. Whereas aleatory uncertainty is always described by probability distributions, epistemic uncertainty may be represented in different ways (e.g., probabilistic or possibilistic), depending on the information and data available.The work presented in this paper addresses the issue of accounting for (in)dependence relationships between epistemically-uncertain parameters. When a probabilistic representation of epistemic uncertainty is considered, uncertainty propagation is carried out by a two-dimensional (or double) Monte Carlo (MC) simulation approach; instead, when possibility distributions are used, two approaches are undertaken: the hybrid MC and Fuzzy Interval Analysis (FIA) method and the MC-based Dempster-Shafer (DS) approach employing Independent Random Sets (IRSs). The objectives are: i) studying the effects of (in)dependence between the epistemically-uncertain parameters of the aleatory probability distributions (when a probabilistic/possibilistic representation of epistemic uncertainty is adopted) and ii) studying the effect of the probabilistic/possibilistic representation of epistemic uncertainty (when the state of dependence between the epistemic parameters is defined).The Dependency Bound Convolution (DBC) approach is then undertaken within a hierarchical setting of hybrid (probabilistic and possibilistic) uncertainty propagation, in order to account for all kinds of (possibly unknown) dependences between the random variables.The analyses are carried out with reference to two toy examples, built in such a way to allow performing a fair quantitative comparison between the methods, and evaluating their rationale and appropriateness in relation to risk analysis.
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Affiliation(s)
- NICOLA PEDRONI
- Energy Department, Politecnico di Milano, Via Ponzio, 34/3, Milano, 20133, Italy
| | - ENRICO ZIO
- Chair of System Science and the Energetic Challenge-Electricitè de France, Ecole Centrale Paris and Supelec Grande Voie des Vignes, 92295, Chatenay Malabry-Cedex, France
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Mauris G. Possibility distributions: A unified representation of usual direct-probability-based parameter estimation methods. Int J Approx Reason 2011. [DOI: 10.1016/j.ijar.2011.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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DESTERCKE SEBASTIEN, DUBOIS DIDIER, CHOJNACKI ERIC. A CONSONANT APPROXIMATION OF THE PRODUCT OF INDEPENDENT CONSONANT RANDOM SETS. INT J UNCERTAIN FUZZ 2011. [DOI: 10.1142/s0218488509006261] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The belief structure resulting from the combination of consonant and independent marginal random sets is not, in general, consonant. Also, the complexity of such a structure grows exponentially with the number of combined random sets, making it quickly intractable for computations. In this paper, we propose a simple guaranteed consonant outer approximation of this structure. The complexity of this outer approximation does not increase with the number of marginal random sets (i.e., of dimensions), making it easier to handle in uncertainty propagation. Features and advantages of this outer approximation are then discussed, with the help of some illustrative examples.
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Affiliation(s)
| | - DIDIER DUBOIS
- Université Paul Sabatier, IRIT/RPDMP, 118 Route de Narbonne, 31062 Toulouse, France
| | - ERIC CHOJNACKI
- Institut de Radioprotection et Sûreté nucléaire, Bât 720, 13115 St-Paul lez Durance, France
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Nassreddine G, Abdallah F, Denoux T. State Estimation Using Interval Analysis and Belief-Function Theory: Application to Dynamic Vehicle Localization. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B (CYBERNETICS) 2010; 40:1205-18. [PMID: 20007051 DOI: 10.1109/tsmcb.2009.2035707] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Ghalia Nassreddine
- UMR CNRS 6599 HEUDIASYC, Université de Technologie de Compiègne, 60205 Compiègne Cedex, France.
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Baudrit C, Hélias A, Perrot N. Joint treatment of imprecision and variability in food engineering: Application to cheese mass loss during ripening. J FOOD ENG 2009. [DOI: 10.1016/j.jfoodeng.2009.01.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Utkin L, Destercke S. Computing expectations with continuous p-boxes: Univariate case. Int J Approx Reason 2009. [DOI: 10.1016/j.ijar.2009.02.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Zhang K, Li H, Achari G. Fuzzy-stochastic characterization of site uncertainty and variability in groundwater flow and contaminant transport through a heterogeneous aquifer. JOURNAL OF CONTAMINANT HYDROLOGY 2009; 106:73-82. [PMID: 19217686 DOI: 10.1016/j.jconhyd.2009.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2008] [Revised: 01/10/2009] [Accepted: 01/14/2009] [Indexed: 05/27/2023]
Abstract
Site variabilities and uncertainties in data and information lead to significant spread in results of groundwater flow and contaminant transport models. A framework for hybrid propagation of random uncertainties represented by probability theory; nonrandom uncertainties represented by fuzzy set theory; and site variabilities represented by geostatistics was developed in this research. A case study was provided to explain the computational algorithm. The methodology presented here can be applied to complex environments where there are site variabilities as well as uncertainties of different kinds. The algorithm is suited when uncertainties in some variables may be best represented as fuzzy numbers whereas in others as probability distributions and both form part of the same governing equation.
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Affiliation(s)
- Kejiang Zhang
- Department of Civil Engineering, University of Calgary, Calgary, AB, Canada T2N 1N4
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Destercke S, Dubois D, Chojnacki E. Unifying practical uncertainty representations – I: Generalized p-boxes. Int J Approx Reason 2008. [DOI: 10.1016/j.ijar.2008.07.003] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Aregui A, Denœux T. Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities. Int J Approx Reason 2008. [DOI: 10.1016/j.ijar.2008.06.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Destercke S, Chojnacki E. Methods for the evaluation and synthesis of multiple sources of information applied to nuclear computer codes. NUCLEAR ENGINEERING AND DESIGN 2008. [DOI: 10.1016/j.nucengdes.2008.02.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Zio E. RISK-INFORMED REGULATION: HANDLING UNCERTAINTY FOR A RATIONAL MANAGEMENT OF SAFETY. NUCLEAR ENGINEERING AND TECHNOLOGY 2008. [DOI: 10.5516/net.2008.40.5.327] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Uncertainty and Sensitivity Analysis for Models of Complex Systems. LECTURE NOTES IN COMPUTATIONAL SCIENCE AND ENGINEERING 2008. [DOI: 10.1007/978-3-540-77362-7_9] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Baudrit C, Guyonnet D, Dubois D. Joint propagation of variability and imprecision in assessing the risk of groundwater contamination. JOURNAL OF CONTAMINANT HYDROLOGY 2007; 93:72-84. [PMID: 17321003 DOI: 10.1016/j.jconhyd.2007.01.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2006] [Revised: 01/11/2007] [Accepted: 01/16/2007] [Indexed: 05/14/2023]
Abstract
Estimating risks of groundwater contamination often require schemes for representing and propagating uncertainties relative to model input parameters. The most popular method is the Monte Carlo method whereby cumulative probability distributions are randomly sampled in an iterative fashion. The shortcoming of the approach, however, arises when probability distributions are arbitrarily selected in situations where available information is incomplete or imprecise. In such situations, alternative modes of information representation can be used, for example the nested intervals known as "possibility distributions". In practical situations of groundwater risk assessment, it is common that certain model parameters may be represented by single probability distributions (representing variability) because there are data to justify these distributions, while others are more faithfully represented by possibility distributions (representing imprecision) due to the partial nature of available information. This paper applies two recent methods, designed for the joint-propagation of variability and imprecision, to a groundwater contamination risk assessment. Results of the joint-propagation methods are compared to those obtained using both interval analysis and the Monte Carlo method with a hypothesis of stochastic independence between model parameters. The two joint-propagation methods provide results in the form of families of cumulative distributions of the probability of exceeding a certain value of groundwater concentration. These families are delimited by an upper cumulative distribution and a lower distribution respectively called Plausibility and Belief after evidence theory. Slight differences between the results of the two joint-propagation methods are explained by the different assumptions regarding parameter dependencies. Results highlight the point that non-conservative results may be obtained if single cumulative probability distributions are arbitrarily selected for model parameters in the face of imprecise information and the Monte Carlo method is used under the assumption of stochastic independence. The proposed joint-propagation methods provide upper and lower bounds for the probability of exceeding a tolerance threshold. As this may seem impractical in a risk-management context, it is proposed to introduce "a-posteriori subjectivity" (as opposed to the "a-priori subjectivity" introduced by the arbitrary selection of single probability distributions) by defining a single indicator of evidence as a weighted average of Plausibility and Belief, with weights to be defined according to the specific context.
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Affiliation(s)
- Cédric Baudrit
- IRIT / UPS, 118 route de Narbonne, 31062 Toulouse, Cedex, France
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