登录注册
Quick Links : Mindat手册The Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
主页关于 MindatMindat手册Mindat的历史版权Who We Are联系我们于 Mindat.org刊登广告
捐赠给 MindatCorporate Sponsorship赞助板页已赞助的板页在 Mindat刊登 广告的广告商于 Mindat.org刊登广告
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
搜索矿物的性质搜索矿物的化学Mineral Visual ExplorerAdvanced Locality Search随意显示任何一 种矿物Random Locality使用minID搜索邻近产地Search Articles搜索词汇表更多搜索选项
搜索:
矿物名称:
地区产地名称:
关键字:
 
Mindat手册添加新照片Rate Photos产区编辑报告Coordinate Completion Report添加词汇表项目
Mining Companies统计会员列表Mineral MuseumsClubs & Organizations矿物展及活动The Mindat目录表设备设置The Mineral QuizTime Machine
照片搜索Photo GalleriesSearch by ColorPhoto Colour Explorer今天最新的照片昨天最新的照片用户照片相集过去每日精选照片相集Photography

Budamala, Venkatesh; Kona, Sai Vikas; Bhowmik, Rajarshi Das; Kim, Hyunglok (2026) Process guided graph-based transformer learning for streamflow predictions in data-sparse river basins. Journal of Hydrology, 676. doi:10.1016/j.jhydrol.2026.135672

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleProcess guided graph-based transformer learning for streamflow predictions in data-sparse river basins
JournalJournal of Hydrology
AuthorsBudamala, VenkateshAuthor
Kona, Sai VikasAuthor
Bhowmik, Rajarshi DasAuthor
Kim, HyunglokAuthor
Year2026 (August)Volume676
PublisherElsevier BV
DOIdoi:10.1016/j.jhydrol.2026.135672Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID20043867Long-form Identifiermindat:1:5:20043867:6
GUID0
Full ReferenceBudamala, Venkatesh; Kona, Sai Vikas; Bhowmik, Rajarshi Das; Kim, Hyunglok (2026) Process guided graph-based transformer learning for streamflow predictions in data-sparse river basins. Journal of Hydrology, 676. doi:10.1016/j.jhydrol.2026.135672
Plain TextBudamala, Venkatesh; Kona, Sai Vikas; Bhowmik, Rajarshi Das; Kim, Hyunglok (2026) Process guided graph-based transformer learning for streamflow predictions in data-sparse river basins. Journal of Hydrology, 676. doi:10.1016/j.jhydrol.2026.135672
In(2026) Journal of Hydrology Vol. 676. Elsevier BV

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 : 10.1080/02626667.2015.1117088

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Black, V., 2014. Water resources management plan for metropolitan North Georgia Metro Water District.
Not Yet Imported: Environmental Processes - journal-article : 10.1007/s40710-020-00468-x

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Gacu, J.G., Monjardin, C.E.F., Mangulabnan, R.G.T., Mendez, J.C.F., 2025. Application of artificial intelligence in hydrological modeling for streamflow prediction in ungauged watersheds: a review. Water, 17, 2722. https://doi.org/10.3390/W17182722.
Gassman (2007) Am. Soc. Agric. Biol. Eng. The soil and water assessment tool: historical development, applications, and future research directions 50, 1211
Not Yet Imported: - journal-article : 10.1080/02626667.2013.803183

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - book-chapter : 10.1007/978-981-10-2984-4_24

If you would like this item imported into the Digital Library, please contact us quoting Book ID 9789811029837
Not Yet Imported: Advances in Water Resources - journal-article : 10.1016/j.advwatres.2024.104694

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.13031/trans.58.10715

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Nearing (2021) Water Resour. Philos. Phenomenol. Res. What role does hydrological science play in the age of machine learning? 57
Not Yet Imported: - journal-article : 10.1061/(ASCE)HE.1943-5584.0000690

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1038/s43017-023-00450-9

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1623/hysj.48.6.857.51421

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Sun (2025) Environ. Sci. Ecotechnol. A hierarchical transformer and graph neural network model for high-accuracy watershed nitrate prediction 28
Xu, Q., Shi, Y., Bamber, J., Tuo, Y., Ludwig, R., Zhu, X.X., 2023. Physics-aware machine learning revolutionizes scientific paradigm for machine learning and process-based hydrology, 1–44.


See Also

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

 
and/or  
版权所有© mindat.org1993年至2026年,除了规定的地方。 Mindat.org全赖于全球数千个以上成员和支持者们的参与。
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.
隐私政策 - 条款和条款细则 - 联络我们 - Report a bug/vulnerability Current server date and time: 2026.6.8 23:08:11
Go to top of page