This study selected 15 key predictors of the maximum of 8-hour averaged ozone (O3) concentration (O3-8h), using the O3 concentration of Haikou and ERA5 reanalysis data from 2015 to 2020, and constructed a multiple linear regression (MLR) model, support vector machine (SVM) model, and BP neural network (BPNN) model, to predict and test the O3-8h concentration of Haikou in 2021. The results showed that the absolute value of correlation coefficients between the O3-8h and related key prediction factors was mainly among 0.2 and 0.507. The 1 000 hPa relative humidity (RH1000), wind direction (WD1000), and 875 hPa meridional wind (v875) showed a good indicative effect on the O3-8h, with the absolute correlation value exceeding 0.4. The three prediction models could predict the seasonal variation in the O3-8h in Haikou, which was larger in the winter half year and smaller in the summer half year. The root mean square error(RMSE) was the smallest (22.29 μg·m-3) in the BPNN model. The correlation coefficients between the predicted values of three statistical models and observations were ranked as 0.733 (BPNN) > 0.724 (SVM) > 0.591 (MLR), all passing the 99.9% significance test. For the prediction of the O3-8h level, we found that TS scores of these three prediction models decreased with the increase in O3-8h concentration level. Relatively, the point over rate and not hit rate increased with the rise in O3-8h concentration level. TS scores of the SVM and BPNN model were relatively larger than those of MLR, especially in the light pollution level with TS scores remaining above 70%, indicating a better prediction capability.
Keywords: BP neural network; Haikou City; assessment of forecast results; multiple linear regression; ozone（O3）; support vector machine.