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Application of XGBoost algorithm in hourly PM2.5 concentration prediction
| Content Provider | Scilit |
|---|---|
| Author | Pan, Bingyue |
| Copyright Year | 2018 |
| Description | Journal: Iop Conference Series: Earth and Environmental Science 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. |
| Related Links | https://iopscience.iop.org/article/10.1088/1755-1315/113/1/012127/pdf http://iopscience.iop.org/article/10.1088/1755-1315/113/1/012127/pdf |
| ISSN | 17551307 |
| e-ISSN | 17551315 |
| DOI | 10.1088/1755-1315/113/1/012127 |
| Journal | Iop Conference Series: Earth and Environmental Science |
| Issue Number | 1 |
| Volume Number | 113 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-02-21 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Earth and Environmental Science Hardware and Architecture Hourly Pm2.5 Concentration Xgboost Algorithm |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Physics and Astronomy Environmental Science |
