Prediction and explanation for ozone variability using cross-stacked ensemble learning model.

ABSTRACT

With the development of monitoring technology, the variety of ozone precursors that can be detected by monitoring stations has been increased dramatically. And this has brought a great increment of information to ozone prediction and explanation studies. This study completes feature mining and reconstruction of multi-source data (meteorological data, conventional pollutant data, and precursors data) by using a machine learning approach, and built a cross-stacked ensemble learning model (CSEM). In the feature engineering process, this study reconstructed two VOCs variables most associated with ozone and found it works best to use the top seven variables with the highest contribution. The CSEM includes three base models random forest, extreme gradient boosting tree, and LSTM, learning the parameters of the model under the integrated training of cross-stacking. The cross-stacked integrated training method enables the second-layer learner of the ensemble model to make full use of the learning results of the base models as training data, thereby improving the prediction performance of the model. The model predicted the hourly ozone concentration with R2 of 0.94, 0.97, and 0.96 for mild, moderate, and severe pollution cases, respectively; mean absolute error (MAE) of 4.48 µg/m3, 5.01 µg/m3, and 8.71 µg/m3, respectively. The model predicted ozone concentrations under different NOx and VOCs reduction scenarios, and the results show that with a 20 % reduction in VOCs and no change in NOx in the study area, 75.28 % of cases achieved reduction and 15.73 % of cases got below 200 µg/m3. In addition, a comprehensive evaluation index of the prediction model is proposed in this paper, which can be extended to any prediction model performance comparison and analysis. For practical application, machine learning feature selection and cross-stacked ensemble models can be jointly applied in ozone real-time prediction and emission reduction strategy analysis.