{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:28:26Z","timestamp":1781886506715,"version":"3.54.5"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Open Research Fund of Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Company Ltd.,","award":["2422020009"],"award-info":[{"award-number":["2422020009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3532473","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T18:59:11Z","timestamp":1737399551000},"page":"15812-15824","source":"Crossref","is-referenced-by-count":5,"title":["Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2228-5425","authenticated-orcid":false,"given":"Ang","family":"Guo","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China, Yangtze Power Company Ltd., Yichang, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanghe","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China, Yangtze Power Company Ltd., Yichang, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Shi","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China, Yangtze Power Company Ltd., Yichang, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8163-347X","authenticated-orcid":false,"given":"Zhenni","family":"Feng","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China, Yangtze Power Company Ltd., Yichang, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s43017-023-00468-z"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture13081508"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.irfa.2024.103096"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2020.09.009"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/nature14956"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-22-0172.1"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-023-02084-9"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100178"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25976"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3002718"},{"key":"ref13","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Bai"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09616-4"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0442(2001)014<1959:CODAET>2.0.CO;2"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2012.01.006"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/eScience.2018.00047"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0097"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/en13164215"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-010-0363-y"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-012-0661-7"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.12.030"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1029\/2020MS002109"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-020-00898-1"},{"key":"ref26","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Shi"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330717"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.120601"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102228"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1029\/2023wr036337"},{"key":"ref33","article-title":"Graph attention networks","author":"Kovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3221316"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124492"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539397"},{"key":"ref38","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017","journal-title":"arXiv:1707.01926"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"ref40","first-page":"11906","article-title":"DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Lan"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3223918"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124648"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1126\/science.1205438"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74048-3_4"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2019.00032"},{"issue":"1","key":"ref47","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume-title":"Proc. ICML","volume":"30","author":"Maas"},{"key":"ref48","volume-title":"Historical Hourly Weather Data 2012\u20132017","author":"Beniaguev","year":"2017"},{"key":"ref49","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014","journal-title":"arXiv:1409.3215"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref51","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2017","journal-title":"arXiv:1709.04875"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25980"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2023.123033"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2023.114247"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.122838"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10848064.pdf?arnumber=10848064","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T17:39:27Z","timestamp":1751305167000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10848064\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3532473","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}