COMPARISON OF DIFFERENT FUZZY AHP METHODOLOGIES IN RISK ASSESSMENT

Authors

  • Andrejs Radionovs Rezekne Academy of Technologies Student of Doctoral program ''Socio-technical systems modeling''
  • Oleg Uzhga-Rebrov Rezekne Academy of Technologies

DOI:

https://doi.org/10.17770/etr2017vol2.2521

Keywords:

fuzzy logic, risk assessment, fuzzy representation of knowledge, fuzzy analytical hierarchy process

Abstract

Being able to evaluate risks is an important task in many areas of human activity: economics, ecology, etc. Usually, environmental risk assessment is carried out on the basis of multiple and sometimes conflicting factors. Using multiple criteria decision-making (MCDM) methodology is one of the possible ways to solve the problem. Methodologies of analytic hierarchy process (AHP) are the most commonly used MCDM methods, which combine subjective and personal preferences in risk assessment process. However, AHP involves human subjectivity, which introduces vagueness type of uncertainty and requires the usage of decision making under those uncertainties. In this paper it was considered to deal with uncertainty by using the fuzzy-based techniques. However, nowadays there exist multiple Fuzzy AHP methodologies developed by different authors. In this paper, these Fuzzy AHP methodologies will be compared, and the most appropriate Fuzzy AHP methodology for the application in case of environmental risks assessment will be offered on the basis of this comparison.

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Author Biographies

  • Andrejs Radionovs, Rezekne Academy of Technologies Student of Doctoral program ''Socio-technical systems modeling''
    Andrejs Radionovs is a doctoral student at Rezekne Academy of Technologies (Latvia). He obtained his M. Sc. degree in Computer Science at Daugavpils University (Latvia). His research interests include information systems, fuzzy information, uncertainty and, in particular, fuzzy set theory and its applications in risk assessment. E-mail: a.radionovs@gmail.com
  • Oleg Uzhga-Rebrov, Rezekne Academy of Technologies
    Oleg Uzhga-Rebrov is a professor at the Faculty of Economics, Rezekne Academy of Technologies (Latvia). He obtained his doctoral degree in Information Systems at Riga Technical University in 1994. His research interests include different approaches to processing incomplete, uncertain and fuzzy information, in particular, fuzzy set theory, rough set theory, as well as fuzzy classification and fuzzy clustering techniques and their applications in bioinformatics. E-mail: rebrovs@tvnet.lv.

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Published

2017-06-15

How to Cite

[1]
A. Radionovs and O. Uzhga-Rebrov, “COMPARISON OF DIFFERENT FUZZY AHP METHODOLOGIES IN RISK ASSESSMENT”, ETR, vol. 2, pp. 137–142, Jun. 2017, doi: 10.17770/etr2017vol2.2521.