Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms

In this study, highly accurate particulate matter (PM10 and PM2.5) predictions were obtained using meteorological prediction data from the local data assimilation and prediction system (LDAPS) and tree-based machine learning (ML). The study area was Seoul, South Korea, and data from July 2018 to Jun...

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Bibliographic Details

Published in:	Atmospheric pollution research Vol. 13; no. 10; p. 101547
Main Authors:	Kim, Bu-Yo, Lim, Yun-Kyu, Cha, Joo Wan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.10.2022
Subjects:	Air quality monitoring Light gradient boosting algorithm Particulate matter prediction PM10 PM2.5 Tree-based machine learning PM10 Tree-based machine learning Air quality monitoring Light gradient boosting algorithm Particulate matter prediction PM2.5
ISSN:	1309-1042, 1309-1042
Online Access:	Get full text
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