A Novel T-Spherical Fuzzy Multi-Criteria Group Decision-Making for Air Quality Index Assessment

Zeeshan Ahmad; Kifayat Ullah; Zeeshan Ali

Authors

Zeeshan Ahmad Department of Mathematics, Riphah International University (Lahore Campus), 54000, Lahore, Pakistan. https://orcid.org/0009-0002-7090-4235 Author
Kifayat Ullah Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105, Tamil Nadu, India. https://orcid.org/0000-0002-1438-6413 Author
Zeeshan Ali Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C. https://orcid.org/0000-0001-7567-3101 Author

Keywords:

Air quality index; Weighted aggregated sum product assessment (WASPAS) method, T-Spherical fuzzy set; Aggregation operators, Multi-criteria group decision-making.

Abstract

In this study, we expose the fact that adequately assessing the air quality index in an atmosphere based on limited observations is essential for meeting environmental management goals. Fuzzy information has proven to be well-suited for handling uncertainty and subjectivity in ecological studies. Researchers use fuzzy set (FS) to address uncertainty in air quality indices. This article presents a novel decision-making approach that evaluates five alternatives against five attributes. For this purpose, decision-makers (Ɗ) used the T-Spherical FS (TSFS) framework in the assessment process. Additionally, we suggest prioritizing attribute weights based on their importance. Further, we consider T-spherical fuzzy weighted averaging (TSFWA) and T-spherical fuzzy weighted geometric (TSFWG) aggregation operators (AOs) within the framework of T-spherical fuzzy (TSF) information, and subsequently apply them to compute the weighted aggregated sum product assessment (WASPAS) technique. The proposed model handles uncertainties in hypothetical air quality index data, and we demonstrate its reliability through a case study and a sensitivity analysis that highlight its effectiveness and enable comparison with earlier work.