PM2.5 volatility prediction by XGBoost-MLP based on GARCH models

In recent, air pollution has a sever impact on public health and economy development throughout the world. Air pollution consists of a variety of harming components, of which fine particulate matter (PM2.5) is considered to be one of the causes of health concerns. Under this circumstance, accurate prediction of atmospheric pollutant concentrations has become a hot research hotspot in the academic field of environment. The frequent changes of different factors cause random fluctuations in the concentration of PM2.5, which brings difficulties to the control of the concentration of air pollutants. By predicting concentration values within different areas and understanding the changes about PM2.5 concentrations, we can effectively warn and take actions to fluctuations in PM2.5 concentrations and help environment policy decision-makers provide sufficient information to guide their decisions. A hybrid model combining XGBoost, four GARCH models and MLP model(XGBoost-GARCH-MLP)is proposed to predict PM2.5 concentration values and volatility. The experimental research results show that the volatility forecasting model proposed in this study has good performance in the long-term forecasting process. If the volatility is used as the PM2.5 concentration prediction benchmark, a better prediction result will be obtained. In conclusion, the model established in this study can more effectively predict PM2.5 concentrations and fluctuations in different regions.

Automated summary

Air pollution has severe impacts on global public health and economic development, prompting the need for accurate prediction of atmospheric pollutant concentrations. The XGBoost-GARCH-MLP hybrid model shows good performance in predicting PM2.5 concentration and volatility, providing valuable insights for environmental policy decision-makers.