Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai
Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai
About this item
Full title
Author / Creator
Ma, Jinghui , Yu, Zhongqi , Qu, Yuanhao , Xu, Jianming and Cao, Yu
Publisher
Cham: Springer International Publishing
Journal title
Language
English
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Publication information
Publisher
Cham: Springer International Publishing
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Scope and Contents
Contents
Air quality forecasting is crucial to reducing air pollution in China, which has detrimental effects on human health. Atmospheric chemical-transport models can provide air pollutant forecasts with high temporal and spatial resolution and are widely used for routine air quality predictions (e.g., 1–3 days in advance). However, the model’s performanc...
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Full title
Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai
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Record Identifier
TN_cdi_proquest_journals_2645202797
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2645202797
Other Identifiers
ISSN
1680-8584
E-ISSN
2071-1409
DOI
10.4209/aaqr.2019.08.0408
