A PM2.5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations
文献类型：期刊论文
| 作者 | Zhang, Qiang1 ; Wu, Shun1; Wang, Xiangwen1; Sun, Binzhen2; Liu, Haimeng3
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| 刊名 | JOURNAL OF CLEANER PRODUCTION
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| 出版日期 | 2020-12-01 |
| 卷号 | 275页码:13 |
| ISSN号 | 0959-6526 |
| 关键词 | PM(2.5)prediction Multi-task deep learning Artificial intelligence Intensive monitoring stations Lanzhou city |
| DOI | 10.1016/j.jclepro.2020.122722 |
| 通讯作者 | Zhang, Qiang(zhangq@nwnu.edu.cn) |
| 英文摘要 | With the deployment and real-time monitoring of a large number of micro air quality monitoring stations, new application scenarios have been provided for the research of air quality prediction methods based on artificial intelligence. Integrating deep learning with multi-task learning, this paper proposes a hybrid model for air quality prediction to leverage data from intensive air quality monitoring stations. The proposed model consists of a shared layer, a task-specific layer, and a multi-loss joint optimization module. It is tested on three monitoring stations located in three different districts of Lanzhou City, China, for PM2.5 concentration prediction. The results show that: (1) When the number of convolutional layers of convolutional neural network in the shared layer and the number of gated recurrent unit layers in the task-specific layer exist in two layers, model performs the best, and its predictability of the optimization algorithm with early-stopping will be significantly improved. (2) Using the proposed model to predict PM2.5 concentration on horizon t + 1, the mean absolute error and root mean square error are 4.54 and 7.96, respectively, indicating better performance in intensive air quality prediction than previous models based on simple hybridization. (3) The predictive performance on different stations is different, and the proposed model performs better than other models when there are large fluctuations and sudden changes in the data. Overall, the proposed model has good temporal stability and generalization ability and provides a new method for air quality prediction in intensive air quality monitoring scenarios. (C) 2020 Elsevier Ltd. All rights reserved. |
| WOS关键词 | SHORT-TERM-MEMORY ; CONVOLUTIONAL NEURAL-NETWORKS ; OZONE CONCENTRATION ; POLLUTION ; EMISSIONS ; CHINA ; GRNN ; AREA |
| 资助项目 | National Natural Science Foundation of China[71764025] |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000579495100022 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156533]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Qiang |
| 作者单位 | 1.Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China 2.Xidian Univ, Coll Econ & Management, Xian 710071, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Qiang,Wu, Shun,Wang, Xiangwen,et al. A PM2.5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations[J]. JOURNAL OF CLEANER PRODUCTION,2020,275:13. |
| APA | Zhang, Qiang,Wu, Shun,Wang, Xiangwen,Sun, Binzhen,&Liu, Haimeng.(2020).A PM2.5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations.JOURNAL OF CLEANER PRODUCTION,275,13. |
| MLA | Zhang, Qiang,et al."A PM2.5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations".JOURNAL OF CLEANER PRODUCTION 275(2020):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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