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Geographic Graph Network for Robust Inversion of Particulate Matters

文献类型：期刊论文

作者Li, Lianfa1,2
刊名REMOTE SENSING
出版日期2021-11-01
卷号13期号:21页码:28
关键词geographic graph hybrid network graph convolution neighborhood feature PM2.5 & nbsp PM10 & nbsp spatiotemporal modeling & nbsp loss constraint ???????
DOI10.3390/rs13214341
通讯作者Li, Lianfa(lilf@igsnrr.ac.cn)
英文摘要Although remote sensors have been increasingly providing dense data and deriving reanalysis data for inversion of particulate matters, the use of these data is considerably limited by the ground monitoring samples and conventional machine learning models. As regional criteria air pollutants, particulate matters present a strong spatial correlation of long range. Conventional machine learning cannot or can only model such spatial pattern in a limited way. Here, we propose a method of a geographic graph hybrid network to encode a spatial neighborhood feature to make robust estimation of coarse and fine particulate matters (PM10 and PM2.5). Based on Tobler's First Law of Geography and graph convolutions, we constructed the architecture of a geographic graph hybrid network, in which full residual deep layers were connected with graph convolutions to reduce over-smoothing, subject to the PM10-PM2.5 relationship constraint. In the site-based independent test in mainland China (2015-2018), our method achieved much better generalization than typical state-of-the-art methods (improvement in R-2: 8-78%, decrease in RMSE: 14-48%). This study shows that the proposed method can encode the neighborhood information and can make an important contribution to improvement in generalization and extrapolation of geo-features with strong spatial correlation, such as PM2.5 and PM10.
WOS关键词SHORT-TERM ; PM2.5 CONCENTRATIONS ; CHINA ; POLLUTION ; CONSTITUENTS ; PERFORMANCE ; REGRESSION ; MORTALITY ; US
资助项目National Natural Science Foundation of China[41471376] ; National Natural Science Foundation of China[42071369] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040501]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000720473400001
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/167830]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Lianfa
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.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		 Li, Lianfa. Geographic Graph Network for Robust Inversion of Particulate Matters[J]. REMOTE SENSING,2021,13(21):28.
APA Li, Lianfa.(2021).Geographic Graph Network for Robust Inversion of Particulate Matters.REMOTE SENSING,13(21),28.
MLA Li, Lianfa."Geographic Graph Network for Robust Inversion of Particulate Matters".REMOTE SENSING 13.21(2021):28.

入库方式： OAI收割

来源：地理科学与资源研究所

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