Physics-Informed Machine Learning for Mapping the Heat Mitigation Potential of Vegetation in Singapore

This study develops a Physics-Informed Neural Network (PINN) model using a multi-layer perceptron (MLP) to estimate vegetation cooling effects, specifically spatially averaged air temperature reduction (ΔT) and vegetation-surrounding UTCI reduction (ΔUTCI), across heterogeneous urban contexts. The model incorporates building, vegetation, and climate features and is trained on ENVI-met simulations from 22 representative sites in Singapore, covering monsoon and inter-monsoon scenarios. These sites, selected through context-based mapping, capture diverse building and greenery configurations. By embedding physics constraints on sensible and latent heat fluxes from the Surface Energy Balance into the loss function, the model captures the overall influence of vegetation at the pedestrian level (2 m), with the physics loss enhancing accuracy and generalizability. A sensitivity analysis and an explanatory study using SHapley Additive exPlanations (SHAP) are conducted to assess the local and global impacts of features. The contributions of building, vegetation, and climate features to vegetation cooling effects are quantified across day/night and monsoon/inter-monsoon seasons. Results indicate that during daytime, ΔT is largely influenced by vegetation features (40%), whereas ΔUTCI is primarily driven by background climate features (47%), with seasonal variations affecting individual feature importance. These insights inform scenario-based urban design guidelines for optimizing vegetation cooling potential. The trained model is applied to built-up areas in Singapore, generating a vegetation cooling distribution map that reveals a mean ΔT of 0.5 °C and a mean ΔUTCI of 2 °C across all sites around noon. Targeted interventions are proposed for areas with suboptimal vegetation cooling performance, providing an urban design solution for leveraging greenery as a passive cooling strategy to mitigate urban heat in high-density cities.