High-Precision Urban Air Quality Prediction Using a LSTM-Transformer Hybrid Architecture

With the acceleration of urbanization, accurate air quality prediction is crucial for environmental governance and public health risk management. Existing prediction methods still face challenges in handling complex time-series dependencies and multi-scale features. In this paper, a hybrid deep learning architecture (LT-Hybrid) based on LSTM and Transformer is proposed for high-precision air quality prediction. The model captures the long-term dependencies of time-series data through a two-layer LSTM structure, models the complex interactions among different environmental factors using a multi-head self-attention mechanism, and improves the training stability through a combination of residual connections and layer normalization. Experiments on an urban air quality dataset, containing nine dimensions of environmental characteristics such as temperature, humidity, PM2.5, etc., show that the LT-Hybrid model achieves an RMSE of 0.1021 and an R² of 0.9382, reducing prediction errors by 13.0% and 5.1% compared to benchmark models of traditional LSTM and XGBoost, respectively. Accurate prediction of air quality indicators provides timely risk assessment for respiratory diseases and cardiovascular conditions, enabling proactive public health interventions. Through systematic ablation experiments and hyperparameter analysis, the validity of each core component of the model is verified, providing a high-precision prediction scheme for environmental monitoring and health risk assessment.

@article{Liu2025,
  title     = {High-Precision Urban Air Quality Prediction Using a LSTM-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    = {Yiming Liu and Mcxin Tee and Liangyan Lu and Fei Zhou and Binggui Lu},
  doi       = {10.14569/IJACSA.2025.0160431},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160431}
}