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A Linguistic-Valued Information Processing Method for Fuzzy Risk Analysis

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Decision Aid Models for Disaster Management and Emergencies

Part of the book series: Atlantis Computational Intelligence Systems ((ATLANTISCIS,volume 7))

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Abstract

Risk analysis is a crucial issue to be handled in disaster management. Fuzzy risk analysis, i.e., risk analysis model using fuzzy set theory aims to assess the risk of hazard event under incomplete or imprecise environment. In practice, the evaluations of risk is always expressed by linguistic values in natural language. In this chapter, we present two approaches for fuzzy risk analysis with linguistic evaluation values. The first approach is based on the unbalanced linguistic weighted geometric operator, which can be used to deal with aggregation of unbalanced linguistic risk evaluation values with numerical weights or linguistic weights. The advantage of the approach is that the evaluation result is linguistic value which is no need of approximation processing and easier to communicate to decision and policy-makers. The other approach is based on linguistic truth-values lattice implication algebra from the logical algebraic point of view. We discuss the operations and special properties of the lattice implication algebra with evaluation-10 linguistic evaluation values. The reasoning and aggregation process directly act on the linguistic evaluation values in the risk analysis process. This approach can better express and handle both comparable and incomparable linguistic information in risk analysis domains. The proposed approaches aim at provide a support for risk analysis in different application context including disaster management under uncertain environment.

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References

  1. Kirchsteiger, C. (1999). On the use of probabilistic and deterministic methods in risk analysis, Journal of Loss Prevention in the Process Industries 12, pp. 399–419.

    Google Scholar 

  2. Rosqvist, T. (2010). On the validation of risk analysis, Reliability Engineering and System Safety 95, pp. 1261–1265.

    Google Scholar 

  3. Weber, P., Medina-Oliva, G., Simon, C. and Iung, B. (2010). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas, Engineering Applications of Artificial Intelligence, (in press, doi:10.1016/j.engappai.2010.06.002).

  4. Chen, S. M. and Wang, C. H. (2009). Fuzzy risk analysis based on ranking fuzzy numbers using α-cuts, belief features and signal/noise ratios, Expert Systems with Applications 36, pp. 5576–5581.

    Google Scholar 

  5. Chen, S. M. (1996). New methods for subjective mental workload assessment and fuzzy risk analysis, Cybernetics and Systems 27, 5, pp. 449–472.

    Google Scholar 

  6. Chen, S. M. and Chen, J. H. (2009). Fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers and interval-valued fuzzy number arithmetic operators, Expert Systems with Applications 36, pp. 6309–6317.

    Google Scholar 

  7. Chen, S. J. and Chen, S. M. (2003). Fuzzy risk analysis based on similarity of generalized fuzzy numbers, IEEE Transactions on Fuzzy Systems 11, 1, pp. 45–56.

    Google Scholar 

  8. Chen, S. J. and Chen, S. M. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers, Applied Intelligence 26, 1, pp. 1–11.

    Google Scholar 

  9. Chen, S. J. and Chen, S. M. (2008). Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers, Computers and Mathematics with Applications 55, 1, pp. 1670–1685.

    Google Scholar 

  10. Tang, T. C. and Chi, L. C. (2005). Predicting multilateral trade credit risks: Comparisons of Logit and fuzzy logic models using ROC curve analysis, Expert Systems with Applications 28, 3, pp. 547–556.

    Google Scholar 

  11. Wang, Y. M. and Elhag, T. M. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment, Expert Systems with Applications 31, 2, pp. 309–319.

    Google Scholar 

  12. Yong, D. and Qi, L. (2005). A TOPSIS-based centroid-index ranking method of fuzzy numbers and its application in decision-making, Cybernetics and Systems 36, 6, pp. 581–595.

    Google Scholar 

  13. Lee, L. and Chen, S. M., (2008). Fuzzy risk analysis based on fuzzy numbers with different shapes, Expert Systems with Applications 34, pp. 2763–2771. November 27, 2012 23:14 Atlantis Press Book - 9.75in x 6.5in book_Vitoriano-Montero 142 Decision Aid Models for Disaster Management and Emergencies.

    Google Scholar 

  14. Pei, Z., Liu, X. and Zou, L. (2010). Extracting Association Rules Based on Intuitionistic Fuzzy Sets, International Journal of Innovative Computing, Information and Control 6, 6, pp. 2567– 2580.

    Google Scholar 

  15. Pei, Z. (2009). Fuzzy risk analysis based on linguistic information fusion, ICIC Express Letters 3, 3A, pp. 325–330.

    Google Scholar 

  16. Sun, F., Li, B., Zou, L. and Pei, Z. (2011). Unbalanced linguistic information in fuzzy risk analysis, ICIC Express Letters 5, 5, pp. 1661–1666.

    Google Scholar 

  17. Pei, Z., Ruan, D., Liu, J. and Xu, Y. (2009). Linguistic Values based Intelligent Information Processing: Theory, Methods, and Applications, Atlantis Computational Intelligence Systems, Vol. 1 (Atlantis Press/World Scientific).

    Google Scholar 

  18. Pei, Z. and Yi, L. (2006). A new aggregation operator of linguistic information and its properties, 2006 IEEE International Conference on Granular Computing, pp. 486–489.

    Google Scholar 

  19. Pei, Z., Ruan, D., Xu, Y. and Liu,J. (2007). Gathering linguistic information in distributed intelligent agent on the internet, Internat.J. Intell. Systems 22, pp. 435–453.

    Google Scholar 

  20. Meng, D. and Pei, Z. (2010). The linguistic computational models based on index of linguistic label, The Journal of Fuzzy Mathematics 18, 1, pp.9–20.

    Google Scholar 

  21. Pei, Z., Ruan, D., Liu, J., Xu, Y. (2012). A linguistic aggregation operator with three kinds of weights for nuclear safeguards evaluation, Knowledge-Based Systems (doi:10.1016/j.knosys.2011.10.016).

  22. Pei, Z. and Shi, P. (2011). Fuzzy risk analysis based on linguistic aggregation operators, International Journal of Innovative Computing, Information and Control 7, 12, pp. 7105–7118.

    Google Scholar 

  23. Okuhara, K., Yeh, K. Y., Shibata, J., Ishii,H. and Hsia, H. C. (2008). Evaluation and assignment of traffic volume for urban planning based on planner and user stances, International Journal of Innovative Computing Information and Control 4, 5, pp. 1151–1160.

    Google Scholar 

  24. Mazurov, A. and Pakshin, P. (2009). Stochastic dissipativity with risk-sensitive storage function and related control problems, ICIC Express Letters 3, 1, pp. 53–60.

    Google Scholar 

  25. Herrera, F. and Martínez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transactions On Fuzzy Systems 8, 6, pp. 746–752.

    Google Scholar 

  26. Herrera, F., Herrera-Viedma, E. and Martinez, L. (2008). A fuzzy linguistic methodology to deal with unbalanced linguistic term sets, IEEE Transactions On Fuzzy Systems 16, 2, pp. 354– 370.

    Google Scholar 

  27. Herrera, F. and Alonso, S. et al, (2009). Computing With Words in Decision Making: Foundations, Trends and Prospects, Fuzzy Optimization and Decision Making 8, 4, pp. 337–364.

    Google Scholar 

  28. Herrera, F. and Martinez, L. (2001). A model based on linguistic 2-tuples for dealing with multi-granularity hierarchical linguistic contexts in multi-expert decision-making, IEEE Trans. Syst., Man Cybern. B, Cybern. 31, 2, pp. 227–234.

    Google Scholar 

  29. Cordón, O., Herrera, F. and Zwir, I. (2001). Linguistic modeling by hierarchical systems of linguistic rules, IEEE Transactions On Fuzzy Systems 10, 1, pp. 2–20.

    Google Scholar 

  30. Mendel, J.M. and Zadeh, L.A. et al., (2010). What Computing with Words means to me, IEEE Computational Intelligence Magazine 5, 1, pp. 20–26.

    Google Scholar 

  31. Ho, N. and Wechler, W. (1990). Hedge algebras: an algebraic approach to structure of sets of linguistic turth values, Fuzzy Sets and Systems 35, pp. 281–293.

    Google Scholar 

  32. Ho, N. and Wechler, W. (1992). Extended hedge algebras and their application to fuzzy logic, Fuzzy Sets and Systems, 52, pp. 259–281.

    Google Scholar 

  33. Ho, N. C. and Long, N. V. (2007). Fuzziness measure on complete hedge algebras and quantifying semantics of terms in linear hedge algebras, Fuzzy Sets and Systems 158, pp. 452–471.

    Google Scholar 

  34. Huang, C. and Ruan, D. (2008). Fuzzy risks and an updating algorithm with new observations, Risk Analysis 28, 3, pp. 681–694.

    Google Scholar 

  35. Kangari, R. and Riggs, L.S. (1989). Construction risk assessment by linguistics, IEEE Transactions on Engineering Management 36, 2, pp. 126–131. November 27, 2012 23:14 Atlantis Press Book - 9.75in x 6.5in book_Vitoriano-Montero Bibliography 143.

    Google Scholar 

  36. Lawry, J. (2004). A framework for linguistic modeling, Artificial Intelligence 155, pp. 1–39.

    Google Scholar 

  37. Shinkai, K. (2008). Decision analysis of fuzzy partition tree applying fuzzy theory, International Journal of Innovative Computing Information and Control 4, 10, pp. 2581–2594.

    Google Scholar 

  38. Xu, Y., Ruan, D., Qin, K. Y. and Liu, J. (2004). Lattice-Valued Logic, Springer.

    Google Scholar 

  39. Xu, Y., Ruan, D., Kerre, E.E., and Liu, J. (2000). α-resolution principle based on lattice-valued propositional logic LPX, Information Sciences 130, pp. 195–223.

    Google Scholar 

  40. Xu, Y., Liu, J., Ruan, D. and Lee, T.T. (2006). On the consistency of rule bases based on latticevalued first-order logic LF(X), International Journal of Intelligent Systems , 21, pp. 399–424.

    Google Scholar 

  41. Xu, Y., Chen, S.W. and Ma, J. (2006). Linguistic truth-valued lattice implication algebra and its Properties, Proc. IMACS Multiconference on “Computational Engineering in Systems Applications” (CESA2006), pp. 1413–1418.

    Google Scholar 

  42. Zadeh, L.A. (1975). The concept of linguistic variable and its application to approximate reasoning, Parts 1, 2, 3, Information Sciences 8, 9, pp. 199–249, pp. 301–357, pp. 43–80.

    Google Scholar 

  43. Zadeh, L.A. (1996). Fuzzy logic = computing with words, IEEE Transactions on Fuzzy Systems 4, 2, pp. 103–111.

    Google Scholar 

  44. Zou, L., Ruan, D., Pei, Z., and Xu, Y. (2011). A Linguistic-Valued Lattice Implication Algebra Approach for Risk Analysis. Journal of Multi-Valued Logic and Soft Computing 17, 293-303.

    Google Scholar 

  45. Zou, L., Ruan, D., Pei, Z. and Xu, Y. (2008). A linguistic truth-valued reasoning approach in decision making with incomparable information, Journal of Intelligent and Fuzzy Systems 19,4–5, pp. 335–343.

    Google Scholar 

  46. Zou, L., Liu, X., Wu, Z. and Xu, Y. (2008). A uniform approach of linguistic truth values in sensor evaluation, Fuzzy Optimization and Decision Making 7, 4, pp. 387–397.

    Google Scholar 

  47. Zou, L., Pei, Z., Liu, X. and Xu, Y. (2009). Semantic of Linguistic Truth-valued Intuitionistic Fuzzy Proposition Calculus, International Journal of Innovative Computing, Information and Control 5, 12(A), pp. 4745–4752.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, China

    Li Zou

  2. School of Mathematics and Computer Engineering, **hua University, Chengdu, 610039, China

    Zheng Pei

  3. SCK • CEN and Ghent University, Boeretang 200, Mol, 2400 Mol, Belgium

    Da Ruan

  4. Intelligent Control Development Center, Southwest Jiaotong University, Chengdu, 610031, China

    Yang Xu

Authors
  1. Li Zou
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  2. Zheng Pei
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  3. Da Ruan
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  4. Yang Xu
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Correspondence to Li Zou .

Editor information

Editors and Affiliations

  1. , Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, Plaza de las Ciencias 3, Madrid, 28040, Spain

    Begoña Vitoriano

  2. , Dept. Of Statistics, Compulutense University, Plaza de Ciencias 3, Madrid, 28040, Spain

    Javier Montero

  3. Center (SCK CEN), Belgian Nuclear Research, Boeretang 200, Mol, 2400, Belgium

    Da Ruan

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Zou, L., Pei, Z., Ruan, D., Xu, Y. (2013). A Linguistic-Valued Information Processing Method for Fuzzy Risk Analysis. In: Vitoriano, B., Montero, J., Ruan, D. (eds) Decision Aid Models for Disaster Management and Emergencies. Atlantis Computational Intelligence Systems, vol 7. Atlantis Press, Paris. https://doi.org/10.2991/978-94-91216-74-9_6

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  • DOI: https://doi.org/10.2991/978-94-91216-74-9_6

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  • Publisher Name: Atlantis Press, Paris

  • Print ISBN: 978-94-91216-73-2

  • Online ISBN: 978-94-91216-74-9

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