A Comparative Analysis of Fuzzy MADM Methods and Fuzzy Inference System in Assessing Air Quality During the Diwali Festival

A. Mohamed Nusaf () and R. Kumaravel
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A. Mohamed Nusaf: Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India
R. Kumaravel: Department of Career Development Centre, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India

New Mathematics and Natural Computation (NMNC), 2025, vol. 21, issue 02, 621-644

Abstract: Assessing air quality with multiple parameters is crucial and demands meticulous analysis due to its profound impact on human health and the environment. Traditional air quality techniques might overlook uncertainties in pollutant data. The fuzzy logic approach adeptly handles such uncertainties. This study thoroughly analyses two approaches: Fuzzy Multi-Attribute Decision Making (MADM) methods and the Fuzzy Inference System (FIS). Fuzzy MADM methods, including the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), are employed with a combined weighting approach involving Analytical Hierarchy Process (AHP) and Entropy. The FIS approach utilizes the Mamdani method built using MATLAB 2021b’s fuzzy logic toolbox. The air quality is assessed for the Diwali festival for the year 2022 in the various regions of Tamil Nadu, India. Spearman’s rank correlation is utilized to determine the result accuracy of fuzzy MADM and FIS. The fuzzy MADM methods attained a higher correlation of 0.88 compared to the Mamdani FIS of 0.84. Fuzzy MADM proves most effective in air quality assessment.

Keywords: Air quality; fuzzy MADM; fuzzy TOPSIS; fuzzy VIKOR; fuzzy inference system; Mamdani fuzzy inference system; Spearman’s rank correlation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S1793005725500280

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