The Role of Machine Learning and Fuzzy Logic in Sustainability Research: A Bibliometric Analysis

Abstract

Sustainability is a multidisciplinary approach that incorporates environmental, economic, and social dimensions, which emerge through the characterization of a shared ideal. This multidisciplinary structure also presents certain challenges; however, integrating machine learning and fuzzy logic methods into this process offers a powerful solution for addressing complex situations and difficulties. It is hypothesized that these methods will facilitate the optimization of strategies in accordance with sustainability goals. The integration of machine learning with fuzzy logic facilitates the creation of strategies that offer long-term benefits in uncertain situations, while large data can be readily analyzed using machine learning methods. It is anticipated that the structures formed through the integration of these three approaches will provide flexible solutions in complex systems characterized by uncertainty. Bibliometric analysis is based on the numerical analysis of the intersection of the determined keywords. In this study, extant studies in the literature are examined through bibliometric analysis, with the aim of analyzing the relationship between sustainability, machine learning, and fuzzy logic approaches. It is hoped that the data obtained within the scope of the study will provide significant contributions and a new perspective to the academic literature within the framework of sustainability, machine learning, and fuzzy logic.