Autoencoder-based deep belief regression network for air particulate matter concentration forecasting
文献类型：期刊论文
| 作者 | Liu, Y; Wang, XX; Xie, JJ; Bai, Y ; 刘宇(东)
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| 刊名 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
 |
| 出版日期 | 2018 |
| 卷号 | 34期号:6页码:3475-3486 |
| 关键词 | Deep belief regression network autoencoder particulate matter meteorological data forecasting |
| ISSN号 | 1064-1246 |
| DOI | 10.3233/JIFS-169527 |
| 文献子类 | Article |
| 英文摘要 | Particulate matter (PM) is one of the most significant air pollutants in recent decades that has tremendous negative effects on the ambient air quality and the public health. Accurate PM forecasting provides a possibility for establishing an early warning system. In this paper, a deep feature learning architecture, i.e., autoencoder-based deep belief regression network (AE-based DBRN), is introduced and utilized to forecast the daily PM concentrations (PM2.5 and PM10). Prior to establishing this model, Pearson correlation analysis is applied to look for the possible input-output mapping, where the input candidate variables contain seven meteorological parameters and PM concentrations within one-day ahead, and the output variables are the local PM forecasts. The addressed model was evaluated by the dataset in the period of 28/10/2013 to 31/8/2016 in Chongqing municipality of China. Moreover, two shallow models, feed forward neural network and least squares support vector regression, were employed for the comparison. The results indicate that the AE-based DBRN model has remarkable better performances among the comparison models in terms of mean absolute percentage error (PM2.5 21.092%, PM10 19.474%), root mean square error (PM2.5 8.600 mu g/m(3), PM10 11.239 mu g/m(3)) and correlation coefficient criteria (PM2.5 0.840, PM10 0.826). |
| 电子版国际标准刊号 | 1875-8967 |
| WOS关键词 | NEURAL-NETWORK ; MODEL ; PREDICTION ; POLLUTION ; ROADSIDE |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000436432400008 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/286064]  |
| 专题 | 高能物理研究所_东莞分部 高能物理研究所_实验物理中心 |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | Liu, Y,Wang, XX,Xie, JJ,et al. Autoencoder-based deep belief regression network for air particulate matter concentration forecasting[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2018,34(6):3475-3486. |
| APA | Liu, Y,Wang, XX,Xie, JJ,Bai, Y,&刘宇.(2018).Autoencoder-based deep belief regression network for air particulate matter concentration forecasting.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,34(6),3475-3486. |
| MLA | Liu, Y,et al."Autoencoder-based deep belief regression network for air particulate matter concentration forecasting".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 34.6(2018):3475-3486. |
入库方式： OAI收割
来源：高能物理研究所
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