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<p>To mitigate the adverse effects of air pollution, accurate PM<sub>2.5</sub> prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM<sub>2.5</sub> prediction model utilizing deep learning techniques, which aims to complement each other’s strengths through model fusion. The model integrates the transformer and LSTM architectures and employs parameter optimization through the particle swarm optimization (PSO) algorithm. The proposed model achieves superior performance by utilizing the gating mechanism of the LSTM model, the positional encoding and self-attention mechanism of the Transformer model, and PSO’s robust optimization capabilities. Experimental results show that the new model outperforms both the traditional LSTM model and the PSO-LSTM model in the PM<sub>2.5</sub> prediction task, and its evaluation metrics, R<sup>2</sup>, MAE, MBE, RMSE, and MAPE, are all improved. Furthermore, the model demonstrates stable performance across different cities and various periods. This study offers a robust approach to improving the accuracy and reliability of PM<sub>2.5</sub> forecasting.</p>

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A deep learning-based hybrid method for PM2.5 prediction in central and western China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月24日

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  • Zuhan Liu,
  • Zihai Fang,
  • Yuanhao Hu

摘要

To mitigate the adverse effects of air pollution, accurate PM2.5 prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM2.5 prediction model utilizing deep learning techniques, which aims to complement each other’s strengths through model fusion. The model integrates the transformer and LSTM architectures and employs parameter optimization through the particle swarm optimization (PSO) algorithm. The proposed model achieves superior performance by utilizing the gating mechanism of the LSTM model, the positional encoding and self-attention mechanism of the Transformer model, and PSO’s robust optimization capabilities. Experimental results show that the new model outperforms both the traditional LSTM model and the PSO-LSTM model in the PM2.5 prediction task, and its evaluation metrics, R2, MAE, MBE, RMSE, and MAPE, are all improved. Furthermore, the model demonstrates stable performance across different cities and various periods. This study offers a robust approach to improving the accuracy and reliability of PM2.5 forecasting.