Air Quality Forecasting Using Machine Learning: A Global perspective
  with Relevance to Low-Resource Settings
Mulomba Mukendi Christian Hyebong Choi
Air pollution stands as the fourth leading cause of death globally. While
extensive research has been conducted in this domain, most approaches rely on
large datasets when it comes to prediction. This limits their applicability in
low-resource settings though more vulnerable. This study addresses this gap by
proposing a novel machine learning approach for accurate air quality prediction
using two months of air quality data. By leveraging the World Weather
Repository, the meteorological, air pollutant, and Air Quality Index features
from 197 capital cities were considered to predict air quality for the next
day. The evaluation of several machine learning models demonstrates the
effectiveness of the Random Forest algorithm in generating reliable
predictions, particularly when applied to classification rather than
regression, approach which enhances the model's generalizability by 42%,
achieving a cross-validation score of 0.38 for regression and 0.89 for
classification. To instill confidence in the predictions, interpretable machine
learning was considered. Finally, a cost estimation comparing the
implementation of this solution in high-resource and low-resource settings is
presented including a tentative of technology licensing business model. This
research highlights the potential for resource-limited countries to
independently predict air quality while awaiting larger datasets to further
refine their predictions.