Machine learning predicts pedestrian wind flow from urban morphology and prevailing wind direction

Abstract Pedestrian-level wind plays a critical role in shaping the urban microclimate and is significantly influenced by urban form and geometry. The most common method for determining spatial wind speed patterns in cities relies on numerical computational fluid dynamics (CFD) simulations, which resolve Navier–Stokes equations around buildings. While effective, these simulations are computationally intensive and require specialised expertise, limiting their broader applicability. To address these limitations, this study proposes a more cost-effective alternative while achieving 90 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="normal">%</mml:mi> </mml:mrow> </mml:math> performance in capturing the mean and maintaining spatial wind patterns captured by CFD. We developed a machine learning (ML) approach with U-net architecture to predict time mean wind speed patterns from prevailing wind directions and three-dimensional urban morphology, which are increasingly available for global cities. The model is trained and tested using a comprehensive dataset of 512 numerical simulations of urban neighbourhoods, representing diverse morphological configurations in cities worldwide. We find that the ML algorithm accurately predicts complex wind patterns, achieving a normalised mean absolute error of less than 10 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="normal">%</mml:mi> </mml:mrow> </mml:math> , which is comparable to wind anemometer measurement in a low wind speed environment. In predicting wind statistics, the ML model also surpasses that of regression models based solely on statistical representations of urban morphology. The R 2 values measuring grid-level agreement between ML and CFD range from 0.94–0.99 and 0.65–0.95 for the idealised and whole datasets, respectively. However, we find that grid-based R 2 is not an effective metric for evaluating the 2D model performance due to localised biases arising from faster wind speed grid regions, which is revealed by the wind probability density function. These findings demonstrate that complex pedestrian wind patterns can be effectively predicted using an image-based ML approach, offering the potential to emulate physics-based large-eddy simulation models, which are computationally expensive, thereby significantly reducing computing costs.