Air Quality Time Series Forecasting Using Machine Learning Algorithms

This study aimed to develop accurate short-term air quality forecasting models for Asunción, Paraguay using machine learning algorithms. The dataset, which consisted of PM concentrations, air quality indices, meteorological variables, and traffic information, spanned a duration of 18 months. Two machine learning algorithms, Support Vector Regression (SVR) and XGBoost, implemented for window-based regression, were optimized using a grid search and Bayesian optimization process. The optimization explored the impact of the look-back window
$(w)$
, sampling frequency
$(f)$
, and prediction horizon
$(h)$
on model performance. The models were evaluated through a hold-out scheme and trained and evaluated for all stations in the network. Results showed that the XGBoost model outperformed the SVR model, with a mean absolute error of 2.774, 4.643, and 7.819 for AQI 2.5 predictions using 6, 12, and 24-hour horizons, respectively. The optimization process significantly improved the performance of both models compared to using default settings. The study's findings have implications for public health decisions and could be used to issue alerts or advisories when air pollution levels are expected to be high.