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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, Qiang2; Wu, Shun2; Wang, Xiangwen2; Sun, Binzhen1; Liu, Haimeng3
刊名JOURNAL OF CLEANER PRODUCTION
出版日期2020-12-01
卷号275页码:13
关键词PM(2.5)prediction Multi-task deep learning Artificial intelligence Intensive monitoring stations Lanzhou city
ISSN号0959-6526
DOI10.1016/j.jclepro.2020.122722
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
WOS记录号WOS:000579495100022
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/156533]  
专题区域可持续发展分析与模拟院重点实验室_外文论文
作者单位1.Xidian Univ, Coll Econ & Management, Xian 710071, Peoples R China
2.Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, 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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