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<p>This paper investigates the effect of barriers of different heights on the distribution of pollutant concentrations in a confined space using numerical simulation and machine learning models. The analysis showed that barriers significantly change the dynamics of pollutant distribution, forming localization and turbulence zones. Three barrier heights were studied: 0.1H, 0.2H, and 0.3H, and their effect was compared with no barrier. Error metrics (MAE, MAPE, and <i>R</i><sup>2</sup>) demonstrated that machine learning models cope well with predicting concentrations for low barriers, but with an increase in their height, the accuracy of the models decreases due to the complexity of aerodynamic processes. The results of the study show that the barrier height has a significant impact on the accuracy of predictions, especially at points with high sensitivity to changes in air flows. The greatest error is observed at a barrier height of 0.2H, while for a barrier of 0.3H, an improvement in the stability of predictions was observed in some cases. The findings highlight the importance of considering the architecture of urban spaces and aerodynamic characteristics by designing protective structures and predictive models. The study demonstrates the applicability of machine learning to the analysis of complex aerodynamic processes, as well as the need for further improvement of models to operate in complex conditions.</p>

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Prediction of the Air Pollution from Emissions in Idealized Urban Street Canyons Using Machine Learning and Computational Fluid Dynamics (CFD) Methods

  • 期刊论文

  • 发表日期：

    2025年8月15日

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  • Alibek Issakhov,
  • Aidana Sabyrkulova,
  • Nurtugan Rysmambetov,
  • Aizhan Abylkassymova

摘要

This paper investigates the effect of barriers of different heights on the distribution of pollutant concentrations in a confined space using numerical simulation and machine learning models. The analysis showed that barriers significantly change the dynamics of pollutant distribution, forming localization and turbulence zones. Three barrier heights were studied: 0.1H, 0.2H, and 0.3H, and their effect was compared with no barrier. Error metrics (MAE, MAPE, and R2) demonstrated that machine learning models cope well with predicting concentrations for low barriers, but with an increase in their height, the accuracy of the models decreases due to the complexity of aerodynamic processes. The results of the study show that the barrier height has a significant impact on the accuracy of predictions, especially at points with high sensitivity to changes in air flows. The greatest error is observed at a barrier height of 0.2H, while for a barrier of 0.3H, an improvement in the stability of predictions was observed in some cases. The findings highlight the importance of considering the architecture of urban spaces and aerodynamic characteristics by designing protective structures and predictive models. The study demonstrates the applicability of machine learning to the analysis of complex aerodynamic processes, as well as the need for further improvement of models to operate in complex conditions.