Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other…

• NS
  Natural Science Foundation of Shandong Province

  Awards: 2023B02, J18RA196, ZR2023MD075, SKLLQG2211
• SK
  State Key Laboratory of Loess and Quaternary Geology