Air Quality Assessment Based on CNN-Transformer Hybrid Architecture

Air quality assessment plays a crucial role in environmental governance and public health decision-making. Traditional assessment methods have limitations in handling multi-source heterogeneous data and complex nonlinear relationships. This paper proposes an air quality assessment model based on a CNN-Transformer hybrid architecture, which achieves end-to-end prediction by integrating CNN's local feature extraction capability with Transformer's advantage in modeling global dependencies. The model employs a three-layer CNN for local feature learning, combined with Transformer's multi-head self-attention mechanism to capture long-range dependencies, and uses multilayer perceptrons for final prediction. Experiments on public datasets demonstrate that compared to traditional machine learning methods and single deep learning models, the proposed hybrid architecture achieves a 10.2 percentage improvement in Root Mean Square Error (RMSE) and a 0.57 percentage point improvement in coefficient of determination (R²). Through systematic ablation experiments, we verify the necessity of each model component, particularly the importance of the CNN-Transformer hybrid architecture, positional encoding mechanism, and multi-layer network structure in enhancing prediction performance. The research results provide an effective deep learning solution for air quality assessment.

@article{Zhang2025,
  title     = {Air Quality Assessment Based on CNN-Transformer Hybrid Architecture},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yuchen Zhang and Rajermani Thinakaran},
  doi       = {10.14569/IJACSA.2025.0160428},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160428}
}