Short-Term Forecasting of Ozone Concentration Using Multivariate Time Series and XGBoost
ZHAO Shenghao
LI Jiaqi
LIU Jun
REN Yan
LIU Yujie
YU Xiaohong
Abstract：Accurate ozone(O3)concentration forecasting is crucial for protecting public health and safety and implementing effective air pollution control measures.Using O3 concentration monitoring data and meteorological data from surface stations,the present study proposed a short-term hourly O3 concentration forecasting method by integrating the XGBoost machine learning algorithm into multivariate time series prediction.Data processing methods,forecasting strategies,and model development and optimization were also discussed.The results indicate that the forecast model developed using the direct multi-step prediction strategy,which can utilize meteorological data from surface stations,performed better.The inclusion of periodic temporal features as input variables effectively enhanced the performance of the forecast model.Finally,the proposed method showed robust performance in the evaluation for the period from August 2020 to April 2021.The RMSE for 1-h,24-h,48-h,and 72-h forecasts were 7.94,16.00,17.49,and 17.72μg m-3,respectively,with corresponding R2 values of 0.94,0.74,0.69,and 0.68.These results indicate that the proposed approach can provide reliable technical support for regional O3 warning and forecasting as well as atmospheric pollution control.
Keywords：ozone concentrationshort-term forecastingtime seriesXGBoost
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 125-135 )