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
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | Process guided graph-based transformer learning for streamflow predictions in data-sparse river basins | ||
| Journal | Journal of Hydrology | ||
| Authors | Budamala, Venkatesh | Author | |
| Kona, Sai Vikas | Author | ||
| Bhowmik, Rajarshi Das | Author | ||
| Kim, Hyunglok | Author | ||
| Year | 2026 (August) | Volume | 676 |
| Publisher | Elsevier BV | ||
| DOI | doi:10.1016/j.jhydrol.2026.135672Search in ResearchGate | ||
| Generate Citation Formats | |||
| Mindat Ref. ID | 20043867 | Long-form Identifier | mindat:1:5:20043867:6 |
| GUID | 0 | ||
| Full Reference | 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 | ||
| Plain Text | 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 | ||
| 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 | |
![]() | Budamala, Venkatesh, Wadhwa, Abhinav, Das Bhowmik, Rajarshi, Mahindrakar, Amit, Satyaji Rao Yellamelli, Ramji, Kasiviswanathan, Kasiapillai S. (2023) Multi-temporal downscaling of daily to sub-daily streamflow for flash flood watersheds at ungauged stations using a hybrid framework. Journal of Hydrology, 625. 130110 doi:10.1016/j.jhydrol.2023.130110 |
![]() | |
![]() | de Paiva, Rodrigo Cauduro Dias, Buarque, Diogo Costa, Collischonn, Walter, Bonnet, Marie-Paule, Frappart, Frédéric, Calmant, Stephane, Bulhões Mendes, Carlos André (2013) Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin. Water Resources Research, 49 (3). 1226-1243 doi:10.1002/wrcr.20067 |
![]() | |
| 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 | |
![]() | Klotz, Daniel, Kratzert, Frederik, Gauch, Martin, Keefe Sampson, Alden, Brandstetter, Johannes, Klambauer, Günter, Hochreiter, Sepp, Nearing, Grey (2022) Uncertainty estimation with deep learning for rainfall–runoff modeling. Hydrology and Earth System Sciences, 26 (6) 1673-1693 doi:10.5194/hess-26-1673-2022 |
![]() | Kratzert, Frederik, Klotz, Daniel, Shalev, Guy, Klambauer, Günter, Hochreiter, Sepp, Nearing, Grey (2019) Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences, 23 (12) 5089-5110 doi:10.5194/hess-23-5089-2019 |
| 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 | |
![]() | Ma, Kai, Feng, Dapeng, Lawson, Kathryn, Tsai, Wen‐Ping, Liang, Chuan, Huang, Xiaorong, Sharma, Ashutosh, Shen, Chaopeng (2021) Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions. Water Resources Research, 57 (5) doi:10.1029/2020wr028600 |
![]() | |
| 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 | |
![]() | Nie, Wanshu, Kumar, Sujay V., Getirana, Augusto, Zhao, Long, Wrzesien, Melissa L., Konapala, Goutam, Ahmad, Shahryar Khalique, Locke, Kim A., Holmes, Thomas R., Loomis, Bryant D., et al. (2024) Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene. Proceedings of the National Academy of Sciences, 121 (45). doi:10.1073/pnas.2403707121 |
| 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, Alexander Y., Jiang, Peishi, Yang, Zong-Liang, Xie, Yangxinyu, Chen, Xingyuan (2022) A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion. Hydrology and Earth System Sciences, 26 (19) 5163-5184 doi:10.5194/hess-26-5163-2022 |
| 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.
