[Application of Support Vector Machine Regression in Ozone Forecasting]

Support vector machine regression (SVMr) was proposed to forecast hourly ozone (O₃) concentrations, daily maximum O₃ concentrations, and maximum 8 h moving average O₃ concentrations (O₃ 8 h) by employing the observations of meteorological variables and O₃ and its precursors during the high O₃ periods from May 20 to August 15, 2016 at an industrial area in Nanjing. The squared correlation coefficient (R²) of the hourly O₃ concentrations forecast was 0.84. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.44×10^-9 and 24.48, respectively. The key factors for the hourly O₃ forecast were the O₃ pre-concentrations, amount of ultraviolet radiation B (UVB), and the NO₂ concentration. The main factors for the O₃ daily maximum forecast were the NO concentrations at 07:00 and the UVB level. Temperature and UVB played an important role in predicting O₃ 8 h. In general, taking precursors into account could increase the accuracy of O₃ prediction by 10%-28%. For O₃ concentration forecasting, SVMr gave significantly better predictions than multiple linear regression methods.