{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:00:48Z","timestamp":1772906448239,"version":"3.50.1"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,15]]},"DOI":"10.1109\/bigdata52589.2021.9671941","type":"proceedings-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T20:39:16Z","timestamp":1642106356000},"page":"1679-1685","source":"Crossref","is-referenced-by-count":4,"title":["DDGNet: A Dual-Stage Dynamic Spatio-Temporal Graph Network for PM<sub>2.5<\/sub>Forecasting"],"prefix":"10.1109","author":[{"given":"Dong","family":"Li","sequence":"first","affiliation":[]},{"given":"Haomin","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yangli-ao","family":"Geng","sequence":"additional","affiliation":[]},{"given":"Xiaobao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qingyong","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"163","article-title":"Pm2. 5-gnn: A domain knowledge enhanced graph neural network for pm2. 5 forecasting","author":"wang","year":"2020","journal-title":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems"},{"key":"ref11","first-page":"418","article-title":"Temporal and spatial distribution of pm2. 5 and pm10 pollution status and the correlation of particulate matters and meteorological factors during winter and spring in beijing","volume":"35","author":"zhao","year":"2014","journal-title":"Huan jing ke xue= Huanjing kexue"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/476"},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10735","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2017","journal-title":"Thirty-First AAAI Conference on Artificial Intelligence"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3008774"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2018.08.032"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/2284333"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-annals-IV-4-W2-15-2017"},{"key":"ref28","article-title":"Divide the gradient by a running average of its recent magnitude. coursera: Neural networks for machine learning","author":"tieleman","year":"2017","journal-title":"Technical Report"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219822"},{"key":"ref27","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2006.11.058"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"12003","DOI":"10.1088\/1755-1315\/78\/1\/012003","article-title":"The impact of meteorological factors on pm2. 5 variations in hong kong","volume":"78","author":"li","year":"2017","journal-title":"IOP Conference Series Earth and Environmental Science"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2014.07.019"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S1352-2310(02)00295-9"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2014.12.003"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.4209\/aaqr.2012.09.0242"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2014.10.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.01.333"},{"key":"ref1","first-page":"475","article-title":"Quantitative relationship between visibility and mass concentration of pm2. 5 in beijing","volume":"18","author":"wang","year":"2006","journal-title":"Journal of Environmental Sciences"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274907"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IC3.2018.00020"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.2978596"},{"key":"ref23","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.2478\/jaiscr-2019-0006"}],"event":{"name":"2021 IEEE International Conference on Big Data (Big Data)","location":"Orlando, FL, USA","start":{"date-parts":[[2021,12,15]]},"end":{"date-parts":[[2021,12,18]]}},"container-title":["2021 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9671263\/9671273\/09671941.pdf?arnumber=9671941","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T22:06:24Z","timestamp":1674425184000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9671941\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/bigdata52589.2021.9671941","relation":{},"subject":[],"published":{"date-parts":[[2021,12,15]]}}}