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Methodology for Environmental Risk Analysis Based on Intuitionistic Fuzzy Values

Author

Listed:
  • Oleg Uzhga-Rebrov

    (Institute of Engineering, Rezekne Academy of Technologies, LV-4601 Rezekne, Latvia)

  • Peter Grabusts

    (Institute of Engineering, Rezekne Academy of Technologies, LV-4601 Rezekne, Latvia)

Abstract

Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of risk analyses. In order to successfully model the existing uncertainties, this paper presents a methodology for ecological risk analysis that is based on input evaluations in the form of intuitionistic fuzzy values (IFVs). The advantage of this approach is the ability to model a wide range of uncertainties in ecological risk analysis tasks.

Suggested Citation

  • Oleg Uzhga-Rebrov & Peter Grabusts, 2023. "Methodology for Environmental Risk Analysis Based on Intuitionistic Fuzzy Values," Risks, MDPI, vol. 11(5), pages 1-22, May.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:5:p:88-:d:1139331
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    References listed on IDEAS

    as
    1. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    2. Terje Aven & Ortwin Renn, 2010. "Risk Management and Governance," Risk, Governance and Society, Springer, number 978-3-642-13926-0, October.
    3. Peide Liu & Junlin Liu & Shyi-Ming Chen, 2018. "Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(1), pages 1-24, January.
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    Cited by:

    1. Jorge De Andrés-Sánchez, 2024. "Calculating Insurance Claim Reserves with an Intuitionistic Fuzzy Chain-Ladder Method," Mathematics, MDPI, vol. 12(6), pages 1-24, March.

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