Ozone level forecasting in Mexico City with temporal features and interactions

Abstract

Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models.

Figures (5)

• Figure 1: Spearman rank correlation matrix between variables.
• Figure 2: Correlation matrix of variables with temporal features
• Figure 3: Importance of the selected features using the Random Forest model (Pollutants and Meteorological factors).
• Figure 4: Scatter plots without temporal features and interactions (approach 1)
• Figure 5: Scatter plots with temporal features and interactions(approach 2)