A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling
This provides a scalable and interpretable solution for urban planners and public health officials in hot desert climates, though it is incremental as it builds on existing physics-informed machine learning methods.
The study tackled the problem of estimating Mean Radiant Temperature for outdoor thermal comfort in urban environments by introducing a Physics-Informed Neural Network approach, achieving an RMSE of 3.50 and an R^2 of 0.88, outperforming conventional deep learning models.
Outdoor thermal comfort is a critical determinant of urban livability, particularly in hot desert climates where extreme heat poses challenges to public health, energy consumption, and urban planning. Mean Radiant Temperature ($T_{mrt}$) is a key parameter for evaluating outdoor thermal comfort, especially in urban environments where radiation dynamics significantly impact human thermal exposure. Traditional methods of estimating $T_{mrt}$ rely on field measurements and computational simulations, both of which are resource intensive. This study introduces a Physics-Informed Neural Network (PINN) approach that integrates shortwave and longwave radiation modeling with deep learning techniques. By leveraging a multimodal dataset that includes meteorological data, built environment characteristics, and fisheye image-derived shading information, our model enhances predictive accuracy while maintaining physical consistency. Our experimental results demonstrate that the proposed PINN framework outperforms conventional deep learning models, with the best-performing configurations achieving an RMSE of 3.50 and an $R^2$ of 0.88. This approach highlights the potential of physics-informed machine learning in bridging the gap between computational modeling and real-world applications, offering a scalable and interpretable solution for urban thermal comfort assessments.