Developing a decision support system for sustainable urban planning using machine learning-based scenario modeling

Urbanization is rapidly transforming cities, posing intricate issues for sustainable urban development. Conventional urban planning techniques frequently encounter difficulties in incorporating several variables, including environmental, social, and economic issues. This research presents an innovative decision support system (DSS) aimed at tackling these difficulties through the application of machine learning and fuzzy decision-making methodologies. The approach utilizes random forest recursive feature elimination (RF-RFE) to determine the most significant criterion from a collection of 15 parameters, such as environmental impact, energy efficiency, social equity, and economic viability. The logarithmic percentage change-driven objective weighting (LOPCOW) approach is employed to determine the weights of these criteria according to their importance. The evaluation based on relative utility and nonlinear standardization (ERUNS) method is employed to rank different urban development methods, utilizing q-rung fuzzy sets (q-ROFS) to address uncertainty and imprecision. The analysis indicates that Green Urbanization is the most advantageous option among the assessed alternatives, demonstrating its compatibility with sustainable development objectives. The proposed DSS integrates machine learning-based feature selection with fuzzy multi-criteria decision-making, providing a comprehensive framework for navigating the intricacies of urban planning and facilitating data-driven, sustainable urban development decisions.