Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Ren, Zhenxing; Ji, Xinxin (2025) On hourly prediction of PM2.5 using spatial–temporal graph convolutional network. Earth Science Informatics, 18 (2). doi:10.1007/s12145-025-01890-1

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleOn hourly prediction of PM2.5 using spatial–temporal graph convolutional network
JournalEarth Science Informatics
AuthorsRen, ZhenxingAuthor
Ji, XinxinAuthor
Year2025 (June)Volume18
Issue2
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s12145-025-01890-1Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID18486579Long-form Identifiermindat:1:5:18486579:6
GUID0
Full ReferenceRen, Zhenxing; Ji, Xinxin (2025) On hourly prediction of PM2.5 using spatial–temporal graph convolutional network. Earth Science Informatics, 18 (2). doi:10.1007/s12145-025-01890-1
Plain TextRen, Zhenxing; Ji, Xinxin (2025) On hourly prediction of PM2.5 using spatial–temporal graph convolutional network. Earth Science Informatics, 18 (2). doi:10.1007/s12145-025-01890-1
In(2025, February) Earth Science Informatics Vol. 18 (2). Springer Science and Business Media LLC

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

Not Yet Imported: - journal-article : 10.1007/s11869-019-00779-5

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Thorax - journal-article : 10.1136/thoraxjnl-2013-204492

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - proceedings-article : 10.1109/INMIC.2005.334494

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Biomedical Signal Processing and Control - journal-article : 10.1016/j.bspc.2014.06.009

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, pp 3844–3852
Dong M, Sun Y, Jin Y, Song C, Zhang X, Luo X (2024) Uncertainty graph convolution recurrent neural network for air quality forecasting. Adv Eng Informatics 62:102651
Not Yet Imported: IEEE Transactions on Signal Processing - journal-article : 10.1109/TSP.2013.2288675

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.apr.2015.09.001

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.knosys.2021.107416

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Liu HX, Han QL, Lu D, Sheng JY, Sui SS, Sun H (2025) Fine-grained graph convolutional network with learning-based bi-relational graph for spatiotemporal forecasting. Expert Syst Appl 265. https://doi.org/10.1016/j.eswa.2024.125959
Not Yet Imported: - journal-article : 10.32604/csse.2022.023882

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Poongadan S, Lineesh MC (2024) Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTM. Neural Process Lett 56(3):164. https://doi.org/10.1007/s11063-024-11622-z
Ren Z, Zhang J, Zhou Y, Ji X (2024) Prediction of PM2.5 with a piecewise affine model considering spatial-temporal correlation. J Intell Fuzzy Syst 46(4):9525–9542
Not Yet Imported: - journal-article : 10.1016/j.apr.2023.101731

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Neural Networks - journal-article : 10.1016/j.neunet.2019.09.033

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.3390/atmos15040418

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.buildenv.2021.108436

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Journal of Cleaner Production - journal-article : 10.1016/j.jclepro.2018.06.068

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - proceedings-article : 10.24963/ijcai.2019/264

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.jclepro.2021.127446

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1080/21642583.2019.1708830

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Applied Soft Computing - journal-article : 10.1016/j.asoc.2019.105972

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Yang X, Li J, Jiang X (2024) Research on information leakage in time series prediction based on empirical mode decomposition. Sci Reports 14(1):28362
Not Yet Imported: - journal-article : 10.1016/j.apr.2020.03.012

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence - proceedings-article : 10.24963/ijcai.2018/505

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: IEEE Transactions on Big Data - journal-article : 10.1109/Tbdata.2023.3277710

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Zeng QL, Cao Y, Fan M, Chen LF, Zhu H, Wang LH ... Liu SZ (2024) Fine particulate matter concentration prediction based on hybrid convolutional network with aggregated local and global spatiotemporal information: a case study in Beijing and Chongqing. Atmos Environ 333: 120647. https://doi.org/10.1016/j.atmosenv.2024.120647
Not Yet Imported: - journal-article : 10.1016/j.eswa.2022.118017

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: Multimedia Tools and Applications - journal-article : 10.1007/s11042-021-10852-w

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/Tits.2019.2935152

If you would like this item imported into the Digital Library, please contact us quoting Journal ID


See Also

These are possibly similar items as determined by title/reference text matching only.

 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 27, 2025 22:47:56
Go to top of page