skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

IOP conference series. Earth and environmental science, 2018-02, Vol.113 (1), p.12127 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1755-1307 ;EISSN: 1755-1315 ;DOI: 10.1088/1755-1315/113/1/012127

Full text available

Citations Cited by
  • Title:
    Application of XGBoost algorithm in hourly PM2.5 concentration prediction
  • Author: Pan, Bingyue
  • Subjects: Air monitoring ; Air quality ; Algorithms ; Computer applications ; Concentration gradient ; Data mining ; Decision trees ; Particulate matter ; Predictions ; Regression analysis ; Regression models ; Support vector machines
  • Is Part Of: IOP conference series. Earth and environmental science, 2018-02, Vol.113 (1), p.12127
  • Description: 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.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1755-1307
    EISSN: 1755-1315
    DOI: 10.1088/1755-1315/113/1/012127
  • Source: Open Access: IOP Publishing Free Content
    AUTh Library subscriptions: ProQuest Central
    IOPscience (Open Access)
    Alma/SFX Local Collection
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait