Data-Driven Urban Heat Mitigation: Integrating CFD and Machine Learning for Adaptive Cooling Strategies

Microclimates play a crucial role in shaping occupant health, thermal comfort, and building energy performance. Urbanized landscapes are characterized by great variation in microclimates, often over very short distances that can be related to the character of the built landscape (including the layout and dimensions of buildings, the properties of construction materials and the natural landscaping).  These microclimates can enhance background climate and weather in cities, affecting the indoor and outdoor environment. The impact of these urban effects are greatest for those living in historically marginalized and low-income communities, where buildings and neighbourhoods are poorly constructed and designed and residents face disproportionate exposure to extreme heat, with limited access to adaptive resources. Addressing these challenges requires an integrated, data-driven approach to urban and building-level heat mitigation.

This study combines real-world weather data collection, Computational Fluid Dynamics (CFD) simulations, and Machine Learning (ML) models to evaluate the efficacy of targeted mitigation strategies in a historically redlined neighborhood in a hot and humid Texas city. At the urban scale, strategies such as increased tree canopy coverage, reflective/cool pavements, high-albedo surfaces, and optimized street layouts are analyzed for their potential to reduce localized heat accumulation. At the building scale, interventions include enhanced insulation, cool roofs, natural ventilation optimization, and external shading solutions. Machine learning algorithms are employed to identify patterns in urban heat distribution, predict temperature fluctuations under different scenarios, and optimize mitigation measures based on building typologies and urban configurations.

The results provide actionable insights for planners and policymakers by quantifying the relative effectiveness of different interventions in reducing urban heat exposure and improving outdoor/indoor thermal comfort. By integrating CFD modeling with ML-driven analysis, this study presents a scalable framework for urban heat mitigation, with implications for equitable and climate-resilient urban planning.