Air Quality Index Prediction Based on Transformer Encoder–CNN–BiLSTM Model

Accurate AQI forecasting is essential for public health and environmental management. However, existing network models for AQI forecasting still exhibit limited predictive accuracy, with insufficient consideration of key influencing factors in current research. Therefore, we present a hybrid model, Transformer Encoder–CNN–BiLSTM. The model not only considers the influence of six major atmospheric pollutant factors (PM2.5, PM10, CO, NO2, SO2, O3), but also offers advantages in modeling long-range dependencies of time series, extracting local features and capturing periodicity and seasonal trends of AQI. Taking Shanghai, China as the research object, the R2, MAE and RMSE of the proposed model are 0.9781, 2.4266 and 4.0321 respectively, far superior to those of other comparison models. In the cross-city validation experiment, the AQI forecasting of Beijing, which has distinct climatic conditions from Shanghai while sharing the same national AQI standard and similar dominant pollutant structure, also demonstrates favorable performance with an R2 of 0.9712, and MAE and RMSE of 3.1275 and 6.6269 respectively. The results indicate that the model can effectively forecast the AQI of Chinese megacities with consistent AQI evaluation criteria.