GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI).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 μmor 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).
Description
Keywords
GIS, Air quality modelling, Spatial prediction, PM10, Machine learning algorithms (MLAs), Selangor State
Citation
Tella, A., & Balogun, A. L. (2021). GIS-based air quality modelling: Spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms. Environmental Science and Pollution Research, 1-17.