Sustainable Urban Innovation and Resilience: Artificial Intelligence and q-Rung Orthopair Fuzzy ExpoLogarithmic Framework

Subramanian Petchimuthu; Balakrishnan Palpandi; Pirabaharan P; Fathima Banu M

doi:10.31181/sdmap21202526

Authors

Subramanian Petchimuthu Department of Science and Humanities (Mathematics), University College of Engineering Nagercoil, Anna University, Tamilnadu, India Author https://orcid.org/0000-0001-5633-7233
Balakrishnan Palpandi Department of Mathematics, University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tamilnadu, India Author https://orcid.org/0000-0002-5769-6227
Pirabaharan P Department of Mathematics, Anna University, Regional Campus-Madurai, Keelakuilkudi, Tamilnadu, India Author https://orcid.org/0000-0003-0216-0481
Fathima Banu M Department of Science and Humanities (Mathematics), University College of Engineering Nagercoil, Anna University, Tamilnadu, India Author https://orcid.org/0009-0008-6299-2734

DOI:

https://doi.org/10.31181/sdmap21202526

Keywords:

q-rung orthopair fuzzy sets, ExpoLogarithmic operations, Multi-criteria decision-making

Abstract

In the face of rapid urbanization and the challenges posed by climate change, achieving sustainability and resilience in urban environments has become imperative. This paper explores a novel approach integrating Artificial Intelligence, Multi-Criteria Decision Making (MCDM), and q-rung orthopair fuzzy ExpoLogarithmic aggregation operators to address the complex dynamics of sustainable urban development. The paper introduces novel ExpoLogarithmic operations rooted in ExpoLogarithmic t-norms within the framework of q-rung orthopair fuzzy sets. By harnessing these operations, the paper proposes two robust aggregation operators under q-rung orthopair fuzzy sets: the q-rung orthopair fuzzy ExpoLogarithmic weighted average and the q-rung orthopair fuzzy ExpoLogarithmic weighted geometric. These aggregation operators demonstrate fundamental properties, including idempotency, monotonicity, and boundedness. To assess the effectiveness of these methods, a MCDM methodology utilizing the recommended aggregation operators is employed. This study integrates a practical demonstration in real-time, with a specific emphasis on enacting appropriate policies for sustainable urban innovation and resilience. Comprehensive analyses, comprising sensitivity, comparative, and performance evaluations of the approaches, are carried out. Additionally, a reflective discourse on the advantages and disadvantages of the proposed aggregation operators is provided alongside the analysis, underscoring the importance of this approach in bolstering city resilience and mitigating environmental impact.

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