Application of XGBoost algorithm in hourly PM2.5 concentration prediction

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance o...

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Veröffentlicht in:IOP conference series. Earth and environmental science Jg. 113; H. 1; S. 12127 - 12133
1. Verfasser: Pan, Bingyue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.02.2018
Schlagworte:
Air monitoring
Air quality
Algorithms
Computer applications
Concentration gradient
Data mining
Decision trees
Particulate matter
Predictions
Regression analysis
Regression models
Support vector machines
ISSN:1755-1307, 1755-1315
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Zusammenfassung:In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/113/1/012127