Articles | Volume 9, issue 2
22 Dec 2023
 | 22 Dec 2023

Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information

Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Santiago Hernández, and Mauricio Ramírez

Data sets

Forecasting 24-hour-averaged PM2.5concentration in the Aburrá Valley using tree-based ML models, global forecasts, and satellite information: Dataset Jhayron S. Pérez-Carrasquilla

Model code and software

jhayron-perez/ForecastPM2.5-SIATA: ForecastPM2.5-SIATA (v1.0.0) Jhayron S. Pérez-Carrasquilla

Short summary
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.