A unified uncertainty-aware decision framework for air quality assessment

Complex real-world decision-making problems are usually distinguished by uncertain, imprecise and multi-source information, especially in environmental assessment cases, as in 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 generalizes the development of an uncertainty-based decision-making framework based on a cubic intuitionistic multi-fuzzy soft set environment. The proposed model becomes effective in representing interval-valued membership/non-membership data and simplifying a number of expert judgments and uncertainty, which is parameterized. Weighted arithmetic and geometric aggregation operators are developed to facilitate multi-criteria decision-making and the key bases of these aggregation operators are evaluated. A systematic decision-making algorithm is based on these operators. The proposed framework's applicability and effectiveness are illustrated using an air quality assessment. Besides, comparative structural and numerical methods with the current fuzzy soft set determinists show that the presented approach offers a better representation of information and consistent ranking ability in the presence of uncertainty. The findings indicate that the suggested framework can be used as a credible, versatile mathematical instrument in complicated environmental decision-making issues.