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Ozone (O3)

We developed machine learning ensemble models (random forest, light gradient boosting, and neural network) to predict monthly O3 concentrations in South Korea.

Model Information
We constructed three machine learning-based models, namely random forest, light gradient boosting, and neural network, to predict monthly O3 averages using a 1 km × 1 km grid during 2002–2020. A total of 112 predictor variables collected from the GEE, SEDAC, regional socioeconomic database, and others were used as input variables, and monthly-average O3 concentrations were predicted as the outcome values. Please refer to the following figures and tables for more specific information.

Table 1. Test results by R2 and rooted mean squared error (RMSE) for each study year and area.

Figure 1. Monitoring Map (Above) vs. Prediction Map (Below)

Figure 2. Trend of Predicted O3 Concentrations (ppb)

Figure 3. Monthly Average O3 Concentration Trend at Monitoring Station

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