New Generalizations of Circular Complex Fuzzy Sets with Gaussian Weighted Aggregation Operators and Applications on Air Quality Assessment
Lobachevskii Journal of Mathematics, cilt.47, sa.5, ss.2314-2332, 2026 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 47 Sayı: 5
- Basım Tarihi: 2026
- Doi Numarası: 10.1134/s1995080226618126
- Dergi Adı: Lobachevskii Journal of Mathematics
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, MathSciNet, zbMATH, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Sayfa Sayıları: ss.2314-2332
- Anahtar Kelimeler: air quality, circular complex -rung orthopair fuzzy set, Gaussian aggregation operators
- Ankara Üniversitesi Adresli: Evet
Özet
Abstract: In this paper, we introduce the concept of circular complex -rung orthopair fuzzy set (CC-ROFS) as a novel generalization that unifies the existing frameworks of circular complex intuitionistic fuzzy sets and complex -rung orthopair fuzzy sets. The proposed approach extends the Gaussian based framework to the CC-ROFSs, aiming to achieve a smoother and statistically meaningful representation of uncertainty. Within this setting, Gaussian based aggregation operators for CC-ROFSs are constructed by employing the Gaussian triangular norm and conorm. Furthermore, Gaussian weighted arithmetic and Gaussian weighted geometric aggregation operators are formulated to enable consistent integration of membership and non-membership information for fuzzy modeling and decision-making. To demonstrate the practical effectiveness and robustness of the proposed framework, the developed CC-ROFS based Gaussian aggregation operators are integrated into an extended weighted aggregated sum product assessment (WASPAS) decision-making model and applied to a real world novel air quality assessment problem. In this context, sixteen major cities representing different geographical regions are evaluated simultaneously with respect to four key air pollutants, namely,,, and. During the application process, the relative air quality performance of the cities is computed, and the obtained results are ranked using a score function. Furthermore, the parametric sensitivity and structural flexibility of the proposed method are examined under different q values and parameters, while the stability of the results is assessed through comparative analyses with alternative MCDM methods.