Liu Zuhan, Fang Zihai, Hu Yuanhao
School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China.
Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang, 330099, China.
Sci Rep. 2025 Mar 24;15(1):10080. doi: 10.1038/s41598-025-95460-6.
To mitigate the adverse effects of air pollution, accurate PM 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 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 prediction task, and its evaluation metrics, R, 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 forecasting.
为减轻空气污染的不利影响，准确的细颗粒物（PM）预测尤为重要。现有模型难以摆脱单个模型自身所具有的局限性。本研究提出一种利用深度学习技术的混合PM预测模型，旨在通过模型融合实现优势互补。该模型整合了Transformer和长短期记忆网络（LSTM）架构，并通过粒子群优化（PSO）算法进行参数优化。所提出的模型通过利用LSTM模型的门控机制、Transformer模型的位置编码和自注意力机制以及PSO强大的优化能力，实现了卓越的性能。实验结果表明，新模型在PM预测任务中优于传统的LSTM模型和PSO-LSTM模型，其评估指标R、平均绝对误差（MAE）、平均偏差误差（MBE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）均有所改善。此外，该模型在不同城市和不同时期均表现出稳定的性能。本研究提供了一种提高PM预测准确性和可靠性的稳健方法。
