A Framework to Predict High-Resolution Spatiotemporal PM(2.5)Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China
文献类型：期刊论文
| 作者 | Zhang, Guangyuan2; Lu, Haiyue3; Dong, Jin1; Poslad, Stefan2; Li, Runkui1,4; Zhang, Xiaoshuai2; Rui, Xiaoping3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-09-01 |
| 卷号 | 12期号:17页码:33 |
| 关键词 | PM2.5 AOD XGBoost prediction deep learning ConvLSTM SARIMA |
| DOI | 10.3390/rs12172825 |
| 通讯作者 | Rui, Xiaoping(ruixp@hhu.edu.cn) |
| 英文摘要 | Air-borne particulate matter, PM2.5(PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM(2.5)distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM(2.5)and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 mu g/m(3)and the highest coefficient of determination regression score function (R-2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 mu g/m(3)compared to SARIMA's 17.41 mu g/m(3). Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM(2.5)in time, and it can also eliminate better the spatial predicted errors compared to SARIMA. |
| WOS关键词 | AEROSOL OPTICAL DEPTH ; PRINCIPAL COMPONENT ANALYSIS ; PARTICULATE MATTER ; PM2.5 ; SATELLITE ; EXPOSURE ; PM10 ; ASSOCIATIONS ; REGRESSION ; POLLUTION |
| 资助项目 | National Key Research and Development Program of China[2017YFB0503605] ; National Natural Science Foundation of China[41771478] ; National Natural Science Foundation of China[41771435] ; Fundamental Research Funds for the Central Universities[2019B02514] ; Beijing Natural Science Foundation[8172046] ; China Scholarship Council (CSC) ; Queen Mary University of London |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000569943700001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Beijing Natural Science Foundation ; China Scholarship Council (CSC) ; Queen Mary University of London |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156875]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Rui, Xiaoping |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Queen Mary Univ London, Sch Elect Engn & Comp Sci, IoT Lab, London E1 4NS, England 3.Hohai Univ, Sch Earth Sci & Engn, Nanjing 211000, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Guangyuan,Lu, Haiyue,Dong, Jin,et al. A Framework to Predict High-Resolution Spatiotemporal PM(2.5)Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China[J]. REMOTE SENSING,2020,12(17):33. |
| APA | Zhang, Guangyuan.,Lu, Haiyue.,Dong, Jin.,Poslad, Stefan.,Li, Runkui.,...&Rui, Xiaoping.(2020).A Framework to Predict High-Resolution Spatiotemporal PM(2.5)Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China.REMOTE SENSING,12(17),33. |
| MLA | Zhang, Guangyuan,et al."A Framework to Predict High-Resolution Spatiotemporal PM(2.5)Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China".REMOTE SENSING 12.17(2020):33. |
入库方式： OAI收割
来源：地理科学与资源研究所
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