Incorporating PM in AQI studies is crucial because of its easily inhalable
micro-size which has adverse impacts on ecology, environment, and human
health. Accurate and timely prediction of the air quality index can ensure
adequate intervention to aid air quality management. Therefore, this study
undertakes a spatial hazard assessment of the air quality index using particulate
matter with a diameter of 10 ?m or lesser (PM10) in Selangor, Malaysia, by
developing four machine learning models: eXtreme Gradient Boosting
(XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB).

Categories: Kualiti, Partikel Terampai, Udara
Tags: Artikel Jurnal, Data Penerbitan, Sulit