ML-Based Framework for Urban Thermal Comfort Measurement and Climate Adaptation: Case Study of Barcelona

Urban heat exposure is one of the most critical and rapidly intensifying impacts of climate change in metropolitan areas. Reliable measurement of outdoor thermal comfort is therefore essential not only for long-term climate-adaptation planning but also for real-time heat-risk management. However, conventional approaches for measuring the Universal Thermal Climate Index (UTCI) rely either on computationally demanding radiative models or on dense in situ monitoring networks, both of which limit scalability and operational deployment at city scale. This study introduces an innovative data-driven framework for urban-scale measurement of mean radiant temperature (t_r) and UTCI using exclusively open-access reanalysis and real-time meteorological data. A supervised Multi-Layer Perceptron (MLP) model is trained to estimate t_r without requiring detailed urban morphology inputs or demanding radiometric instrumentation. The framework achieves high predictive performance (R² = 0.99; RMSE = 1.4 °C for t_r) and computational efficiency suitable for real-time applications. Another key innovation of this study lies in the comfort-vulnerability integration. The estimated outdoor thermal comfort (i.e., UTCI) is combined with socio-demographic vulnerability measurements within a unified Urban Heat Risk Index (UHRI). Applied to the Besòs district in Barcelona, the framework identifies hotspots where thermal stress and social vulnerability converge, supporting urban resilience and adaptation strategies. A Monte Carlo sensitivity and uncertainty-propagation analysis further showed limited error propagation across the framework, with standard uncertainties of 0.88 °C for MLP-based t_r, 0.67 °C for UTCI, and 0.017 for UHRI.