[Predictive Model for O₃ in Shanghai Based on the KZ Filtering Technique and LSTM]

In this study, a Kolmogorov-Zurbenko （KZ） filter was proposed to decompose the original ozone （O₃） sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy （mRMR） feature selection technique was combined with the support vector regression （SVR） approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network （LSTM） was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan （urban area）, Pudong-Chuansha （suburban area）, and Dianshan Lake （suburban area） in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O₃ baseline and short-term components, as chosen by feature screening. The R² values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m^-3, respectively. These findings suggest that decomposing the original O₃ sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the R² and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.