Research on haze prediction method of Xianyang City based on STL decomposition and FEDformer

Abstract

Due to the continuous impact of haze weather, Xianyang city's air quality has ranked in the bottom three of the province for three consecutive years. This has led to an urgent need to improve air quality. Haze pollution prediction is of great practical significance. By timely and accurate prediction of haze pollution, the government and relevant institutions can take necessary measures to improve air quality and protect the ecosystem. Although the traditional RNN and LSTM models can effectively capture the time sequence information in the haze data over the years for prediction, it is still difficult to achieve accurate prediction due to the complexity of haze prediction. In this study, 8769 pieces of heterogeneous data were successfully collected using multi-source big data acquisition technology. A series of pre-processing operations, including data conversion and dimensionality reduction, were performed on different data such as AQI, PM2.5, PM10, SO2, NO2, CO and O3. The method of big data fusion and deep learning is adopted to integrate haze data and discover hidden rules and trends in it. Finally, based on FEDformer model and STL time series decomposition method, the prediction model was established in this study, which achieved significant improvement in both short - and long-term time series prediction problems.