Identifying Priority Areas for Planning Urban Green Infrastructure: A Fuzzy Artificial Intelligence-Based Framework

Urban green infrastructure (UGI) plays a key role in fostering sustainability, resilience, and ecological balance in cities. However, the task of identifying priority areas for UGI implementation remains complex due to the multifactorial nature of urban systems and prevailing uncertainties. This study proposes a fuzzy inference system (FIS)-based framework composed of seven interconnected modules designed to assess diverse criteria, including flood vulnerability, water quality, habitat connectivity, vegetation condition, and social vulnerability. The model was applied in the urban watersheds of São José dos Campos, Brazil, a municipality recognized for its smart city initiatives and urban environmental complexity. Through the integration of multi-criteria spatial data, the framework effectively prioritized urban areas, highlighting critical zones for extreme event mitigation, water quality preservation, habitat conservation, and recreational space provision. The case study demonstrated that São José dos Campos, with an 11.73% urbanized area and 737,310 inhabitants, benefits from targeted UGI typologies, including sustainable drainage systems and green public spaces, aligning infrastructure interventions with specific spatial demands. Notably, the expert validation process involving 18 multidisciplinary specialists confirmed the model’s relevance and coherence, with the majority classifying the outcomes as “highly coherent”. The system’s modular structure, use of triangular membership functions, and incorporation of the gamma operator allow for adaptable prioritization across different planning horizons. By offering a transparent, expert-validated, and data-driven approach, the proposed method advances evidence-based decision-making and equips planners with a practical tool for UGI implementation in dynamic urban contexts.