PM_2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK

The forecast of PM_2.5 concentration plays an important role in air pollution control and improvement of environmental quality. Affected by multiple factors such as changes in meteorological conditions and air pollutants emissions, the PM_2.5 forecast was usually susceptible to sudden changes and noise data. Therefore, in-depth exploration of the PM_2.5 concentration change law and modeling robust prediction models have become key steps in this task. Based on the analysis of the correlation between meteorological conditions and atmospheric pollutants on PM_2.5, a staged temporal-attention network (STAN) was proposed. This method combined a multi-stage attention module and a recurrent neural network (RNN) to model the cross-influence of meteorological factors and atmospheric pollutants on PM_2.5 concentration. Statistical analysis of the absolute error values of the prediction results of Beijing, Shanghai, and Guangzhou showed the following results:1) compared with the widely used support vector machine (SVM), long short-term memory (LSTM), and multilayer perceptron (MLP), the performance of the proposed method increased more than 10%. 2) compared with the latest fusion model U-net, the proposed STAN still achieved a decrease in the error of 7%. Taking Beijing's winter forecast results as an example for statistical analysis, the fitting coefficient between the predicted value of STAN and the measured value could reach 95.2%. In the robustness analysis, it was found that the error increased by only 9.3% when the proposed method ran on data with 10% noise. The above results proved that the combination of the attention mechanism and the temporal learning module could deeply mine the change law of PM_2.5 and suppress noise data. It also showed that the STAN could achieve the robust prediction of PM_2.5 concentration.