Scholarlink

This project represents a significant foray into the intersection of computational fluid dynamics (CFD), machine learning, and environmental science [1]. By integrating CFD results with the predictive capabilities of computer vision and AI, we have crafted a multifaceted approach that not only serves environmental sciences by forecasting air quality but also advances the field of machine learning with its interdisciplinary applications [2]. The developed model stands as a testament to this synergy, exhibiting high levels of accuracy in its predictions, albeit with occasional outliers. Recognizing the model’s substantial promise, we are committed to its ongoing refinement. Future efforts will be channeled into expanding its data foundation and exploring innovative algorithmic strategies, underscoring our long-term commitment to enhancing the project's contribution to both scientific domains. This sustained investment is poised to solidify and extend the practical and theoretical benefits of our interdisciplinary methodology.

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A Smart Air Quality Analysis and Pollutant Diffusion Detection and Prediction System Based on Tree Canopy Shape Research Using Machine Learning and Artificial Intelligence

  • 会议论文

  • 发表日期：

    2025年

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  • Mingyuan Liu,
  • Ang Li

摘要

This project represents a significant foray into the intersection of computational fluid dynamics (CFD), machine learning, and environmental science [1]. By integrating CFD results with the predictive capabilities of computer vision and AI, we have crafted a multifaceted approach that not only serves environmental sciences by forecasting air quality but also advances the field of machine learning with its interdisciplinary applications [2]. The developed model stands as a testament to this synergy, exhibiting high levels of accuracy in its predictions, albeit with occasional outliers. Recognizing the model’s substantial promise, we are committed to its ongoing refinement. Future efforts will be channeled into expanding its data foundation and exploring innovative algorithmic strategies, underscoring our long-term commitment to enhancing the project's contribution to both scientific domains. This sustained investment is poised to solidify and extend the practical and theoretical benefits of our interdisciplinary methodology.