CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Approach to Deal with Aleatory and Epistemic Uncertainty within the Same Framework: Case Study in Risk Assessment

by Palash Dutta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 12
Year of Publication: 2013
Authors: Palash Dutta
10.5120/13916-1873

Palash Dutta . An Approach to Deal with Aleatory and Epistemic Uncertainty within the Same Framework: Case Study in Risk Assessment. International Journal of Computer Applications. 80, 12 ( October 2013), 40-45. DOI=10.5120/13916-1873

@article{ 10.5120/13916-1873,
author = { Palash Dutta },
title = { An Approach to Deal with Aleatory and Epistemic Uncertainty within the Same Framework: Case Study in Risk Assessment },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 12 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number12/13916-1873/ },
doi = { 10.5120/13916-1873 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:24.742288+05:30
%A Palash Dutta
%T An Approach to Deal with Aleatory and Epistemic Uncertainty within the Same Framework: Case Study in Risk Assessment
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 12
%P 40-45
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Risk assessment is an important and popular aid in the decision making process. The aim of risk assessment is to estimate the severity and likelihood of harm to human health from exposure to a substance or activity that under plausible circumstances can cause to human health. In risk assessment, it is most important to know the nature of all available information, data or model parameters. More often, it is seen that available information model parameters, data are usually tainted with aleatory and epistemic uncertainty or both type of uncertainty. When some model parameters are affected by aleatory uncertainty and other some parameters are affected by epistemic uncertainty, how far computation of the risk is concern, one can either transform all the uncertainties to one type of format or need for joint propagation of uncertainties. In this paper, an effort has been made to combine probability distributions, normal fuzzy numbers and generalized interval valued fuzzy numbers (IVFNs) within the same framework.

References
  1. Anoop M. B. , Balaji Rao K. , Gopalakrishnan S. , Conversion of probabilistic information into fuzzy sets for engineering decision analysis. Comp. and Struct. 2006, 84(3–4): 141–155
  2. Anoop M. B. , Balaji Rao K. , Lakshmanan N. Safety assessment of austenitic steel nuclear power plant pipelines against stress corrosion cracking in the presence of hybrid uncertainties. Int. J. Pres. Vessels Piping. (2008),85(4): 238–247
  3. Baraldi, P. & Zio, E. , A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis. Risk Analysis, (2008. )28 (5): 1309-1326.
  4. Baudrit C. , Dubois D. , Guyonnet D. , Fargier H. , Joint Treatment of imprecision and Randomness in Uncertainty Propagation, Proc. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, (2004) 873-880.
  5. Baudrit C. , Dubois D. , Guyonnet D. , Joint Propagation and Exploitation of Probabilistic and Possibilistic Information in Risk Assessment, IEEE Transaction on Fuzzy Systems, 14 (2006) 593-608.
  6. Baudrit, C. & Dubois, D. , Practical Representations of Incomplete Probabilistic Knowledge. Computational Statistics & Data Analysis, (2006. ) 51 (1): 86-108.
  7. Baudrit, C. , Dubois, D. , Perrot, N. Representing parametric probabilistic models tainted with imprecision. Fuzzy Sets and System, (2008) 159: 1913-1928.
  8. Dubois D, Prade H, Sandri S. , Onpossibility/probability transformations, (1993) In:RLowen,MRoubens (Eds. ) Fuzzy Logic. (Dordrecht: Kluwer Academic Publishers).
  9. Dutta P. , Ali T. , A Hybrid Method to Deal with Aleatory and Epistemic Uncertainty in Risk Assessment, International Journal of Computer Applications, (2012) 42: 37-44.
  10. Dutta P. , Ali T. , Fuzzy Focal Elements in Dempster-Shafer Theory of Evidence: Case study in Risk Analysis, International Journal of Computer application, (2011b) 34: 46-53.
  11. Dutta P. , Boruah H. , Ali T. , Fuzzy Arithmetic with and without using ?-cut method: A Comparative Study, International Journal of Latest Trends in Computing. (2011a) 2: 99-107.
  12. EPA U. S. , Risk Assessment Guidance for Superfund, Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment). Office of Emergency and Remedial Response, EPA/540/R/99/005, Interim, Review Draft. United States Environmental Protection Agency. September 2001.
  13. Flage, R. , Baraldi, P. , Zio, E. , Aven, T. , Possibilityprobability transformation in comparing different approaches to the treatment of epistemic uncertainties in a fault tree analysis. In B. Ale, I. A. Papazoglu, E. Zio (Eds. ), Reliability, Risk and Safety - Proceedings of the ESREL, Conference,Rhodes, Greece (2010). : 714-721.
  14. Flage, R. , Baraldi, P. , Zio, E. , Aven, T. , Probabilistic and possibilistic treatment of epistemic uncertainties in fault tree analysis. (2011) Submitted to Risk analysis.
  15. Gehrke M. , Walker C. , Walker E. , Some comments on interval valued fuzzy sets, Int. Jour. Intelligent Systems, (1996) 11: 751-759.
  16. Guyonnet D. , Bourgine B. , Dubois D. , Fargier H. , Côme B. , Chilès J. P. , Hybrid approach for addressing uncertainty in risk assessments, Journal of Environmental Engineering, (2003) 126: 68-78.
  17. Haldar A, Reddy R K. , A random-fuzzy analysis of existing structures. Fuzzy Sets Syst. (1992) 48:201–210
  18. Helton J C, OberkampfWL. , Alternative representations of epistemic uncertainty. Reliab. Engng. Syst. Safety (2004) 85:1–3
  19. Kentel E. , Aral M. M. , Probalistic-fuzzy health risk modeling, Stoch Envir Res and Risk Ass (2004) 18: 324-338.
  20. Li J. , Huang G. H. , Zeng G. M. , Maqsood I. , Huang Y. F. , An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination, Journal of Environmental Management, (2007) 82: 173-188.
  21. Limbourg, P. and de Rocquigny, E. , Uncertainty analysis using evidence theory – confronting level-1and level-2 approaches with data availability and computational constraints, Reliability Engineering andSystem Safety, (2010) 95:550-564.
  22. Maxwell R. M. , Pelmulder S. D. , Tompson A. F. B. , Kastenberg W. E. , On the development of a new methodology for groundwater-driven health risk assessment, Water Resources Res, (1998) 34: 833–847.
  23. Pedronia N. , Zioa E. , Ferrariob E. , Pasanisid A. , Couplet M. , Propagation of aleatory and epistemic uncertainties in the model for the design of a flood protection dike, "PSAM 11 & ESREL (2012), Helsinki : Finland.
  24. Pedronia N. , Zioa E. , Ferrariob E. , Pasanisid A. , Couplet M. Hierarchical propagation of probabilistic and non-probabilistic uncertainty in the parameters of a risk model, Computers & Structures, (2013): 1-15.
  25. Rao, K. D. , Kushwaha, H. S. , Verma, A. K. , Srividya A. Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies. Reliability Engineering and System Safety, (2007) 92: 947-956.
  26. Sambuc R. , "Function ?-Flous, Application a l'aide au Diagnostic en Pathologie Thyroidienne", These de DoctoratenMedicine,UniversityofMarseille (1975).
  27. Zeng, W. , Shi, Y. (2005), 'Note on interval-valued fuzzy set', Lecture notes in computer science, 3613, 20–25.
  28. Zeng, W. & Li, H. (2006b), 'Representation theorem of interval-valued fuzzy set', Uncertainty,Fuzziness and Knowledge-Based Systems 14(3), 259–269.
  29. Zeng, W. , Li, H. , Zhao, Y. & Yu, X. (2007), Extension Principle of Lattice-Valued Fuzzy Set, in 'Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on', Vol. 1.
Index Terms

Computer Science
Information Sciences

Keywords

Aleatory & Epistemic Uncertainty Fuzzy Set Generalized Fuzzy Number Interval Valued Fuzzy Numbers Hybrid Method Risk Assessment. .

Powered by PhDFocusTM