Abstract

Rapid urbanization has intensified air pollution and climate-related risks, challenging sustainable city planning. Green infrastructure (GI) has emerged as a vital strategy for mitigating these environmental stressors. However, selecting optimal GI interventions requires the integration of multiple, often conflicting, criteria such as pollution reduction efficiency, cost-effectiveness, and resilience to climate variability. Conventional decision-making approaches in urban planning often struggle to handle the uncertainty and complexity inherent in multi-criteria environmental assessments. This limitation hampers the identification of effective GI solutions tailored to specific urban contexts, leading to suboptimal pollution and climate risk mitigation. This study proposes a Deep Neural Fuzzy Multi-Criteria Decision Support System (DNF-MCDSS) for prioritizing GI strategies in urban environments. The framework combines fuzzy logic with deep neural networks to model uncertainty and non-linear relationships among environmental, social, and economic criteria. Input data covering air quality indices, climatic variables, land use patterns, and socioeconomic factors are processed through the hybrid network to generate a ranked list of GI interventions. The model’s performance is evaluated using case studies from metropolitan regions, with validation against expert assessments and conventional multi-criteria decision methods. Experimental results demonstrate that the proposed DNF-MCDSS consistently identifies GI strategies that maximize pollution reduction while enhancing climate resilience. For example, green roofs, urban tree corridors, and constructed wetlands were prioritized in scenarios with high pollution loads and extreme heat events. Compared with traditional weighted-sum and AHP methods, the framework achieved a 15–20% improvement in alignment with expert recommendations, showing its ability to capture complex interdependencies and uncertainty in urban environmental planning.

Authors

Someshwar Siddi¹, P.M. Sithar Selvam²
St. Martin’s Engineering College, India¹, KCG College of Technology, India²

Keywords

Deep Neural Networks, Fuzzy Logic, Multi-Criteria Decision Support, Green Infrastructure, Urban Pollution Mitigation