Air, an essential natural resource, has been compromised in terms of quality by economic
activities. Considerable research has been devoted to predicting instances of poor air
quality, but most studies are limited by insufficient longitudinal data, making it difficult to
account for seasonal and other factors. Several prediction models have been developed
using an 11-year dataset collected by Taiwan's Environmental Protection Administration
(EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural …

The purpose of this study was to compare different machine learning models for predicting
daily air quality index (AQI) and evaluating air quality grade (AQG). The study used publicly
available data from 2014 to 2019 for six pollutants (PM10, PM2. 5, NO2, SO2, CO, O3). Four
models (random forest (RF), gradient boosting (GB), Lasso Regression (LASSO), and the
Stacked Regressor) were used for predicting AQI, while six models (K-Nearest Neighbors
(KNN), support vector machines (SVM), decision tree (DT), multilayer perceptron (MLP) …