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<p>Complex real-world decision-making problems are usually distinguished by uncertain, imprecise, and multi-source information, especially in environmental assessment cases such as air quality assessment. To deal with such issues it is important to have strong mathematical models that can incorporate interval uncertainty, expert hesitation, and multiple criteria in a single decision-making framework. This paper presents a cubic intuitionistic multi-fuzzy soft set (CIMFSS)-based uncertainty-aware decision framework. The proposed model effectively represents interval-valued membership and non-membership data while incorporating multiple expert judgments and parameterized uncertainty. Weighted arithmetic and geometric aggregation operators CIMFSWAA and CIMFSWGA are developed to facilitate multi-criteria decision-making, and their key mathematical properties (idempotency, boundedness, monotonicity) are rigorously established. A systematic decision-making algorithm based on entropy-based objective weighting and distance measures is presented. The proposed framework is illustrated through a multi-city air quality assessment case study. Comparative structural and numerical analyses with existing fuzzy soft set models demonstrate that the presented approach offers superior information representation and consistent ranking stability under uncertainty.</p>

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A unified uncertainty-aware decision framework for air quality assessment

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Fatima Razaq,
  • Muhammad Saeed

摘要

Complex real-world decision-making problems are usually distinguished by uncertain, imprecise, and multi-source information, especially in environmental assessment cases such as air quality assessment. To deal with such issues it is important to have strong mathematical models that can incorporate interval uncertainty, expert hesitation, and multiple criteria in a single decision-making framework. This paper presents a cubic intuitionistic multi-fuzzy soft set (CIMFSS)-based uncertainty-aware decision framework. The proposed model effectively represents interval-valued membership and non-membership data while incorporating multiple expert judgments and parameterized uncertainty. Weighted arithmetic and geometric aggregation operators CIMFSWAA and CIMFSWGA are developed to facilitate multi-criteria decision-making, and their key mathematical properties (idempotency, boundedness, monotonicity) are rigorously established. A systematic decision-making algorithm based on entropy-based objective weighting and distance measures is presented. The proposed framework is illustrated through a multi-city air quality assessment case study. Comparative structural and numerical analyses with existing fuzzy soft set models demonstrate that the presented approach offers superior information representation and consistent ranking stability under uncertainty.