Performance Evaluation of PM<sub>2.5</sub> Forecasting Using SARIMAX and LSTM in the Korean Peninsula

Performance Evaluation of PM<sub>2.5</sub> Forecasting Using SARIMAX and LSTM in the Korean Peninsula

Air pollution, particularly fine particulate matter (PM<sub>2.5</sub>), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (...

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Bibliographic Details
Main Authors: Chae-Yeon Lee, Ju-Yong Lee, Seung-Hee Han, Jin-Goo Kang, Jeong-Beom Lee, Dae-Ryun Choi
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Atmosphere
Subjects:
PM<sub>2.5</sub> forecasting
LSTM
SARIMAX
air quality prediction
deep learning
statistical modeling
Online Access:https://www.mdpi.com/2073-4433/16/5/524
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