Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea.

ABSTRACT: Amidst recent industrialization in South Korea, Seoul has experienced high levels of air pollution, an issue that is magnified due to a lack of effective air pollution prediction techniques. In this study, the Prophet forecasting model (PFM) was used to predict both short-term and long-term air pollution in Seoul. The air pollutants forecasted in this study were PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO, air pollutants responsible for numerous health conditions upon long-term exposure. Current chemical models to predict air pollution require complex source lists making them difficult to use. Machine learning models have also been implemented however their requirement of meteorological parameters render the models ineffective as additional mode

SUBMITTER: Shen J 

PROVIDER: S-EPMC7500321 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature