Predictive Urban Ecologies: Integrating AI, Environmental Sensing and Adaptive resilience strategies

Urban districts affected by social fragility and environmental degradation increasingly require predictive and adaptive tools capable of addressing intensifying climate pressures and complex ecological dynamics. This contribution presents a multilayer framework for assessing and enhancing urban environmental resilience by integrating AI-driven environmental analysis, distributed sensing networks and predictive ecological modelling. The proposed approach correlates real-time data on air quality, urban microclimate, mobility flows, land use and public-space conditions with socio-territorial indicators, enabling the early detection of environmental stress patterns in vulnerable neighbourhoods. Machine learning algorithms are employed to identify latent ecological relationships and support dynamic, data-driven interpretations of urban ecosystem behaviour. Experimental application of the framework, based on simulated scenarios and existing urban datasets, indicates a potential 20–25% reduction in urban heat island intensity, improvements in local ecological continuity, and an increase of over 30% in environmental risk prediction accuracy when compared to conventional static assessment methods. These quantitative results demonstrate how AI-based models can effectively support targeted interventions and evidence-based adaptive resilience strategies. The proposed model integrates environmental and socio-territorial indicators within a single dynamic predictive system, oriented towards decision support for urban resilience. By integrating quantitative indicators into adaptive decision-making processes, the research contributes to advancing next-generation approaches for managing environmental vulnerability in contemporary cities.