Ozone level forecasting in Mexico City with temporal features and interactions
This work addresses air pollution forecasting for public health in Mexico City, but it is incremental as it focuses on feature engineering with existing methods.
The study tackled the problem of forecasting tropospheric ozone levels in Mexico City by comparing regression models with and without temporal features and interactions, finding that including these features improved model accuracy.
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.