Prediction of atmospheric PM_2.5 level by machine learning techniques in Isfahan, Iran.

ABSTRACT: With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM_2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM_2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and

SUBMITTER: Mohammadi F 

PROVIDER: S-EPMC10808097 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature