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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Veröffentlicht in:	Atmospheric pollution research Jg. 13; H. 10; S. 101547
Hauptverfasser:	Kim, Bu-Yo, Lim, Yun-Kyu, Cha, Joo Wan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2022
Schlagworte:	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-Zugang:	Volltext
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