Articles | Volume 29, issue 23
https://doi.org/10.5194/hess-29-6811-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6811-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
01 Dec 2025
Research article | Highlight paper |
| 01 Dec 2025
| 01 Dec 2025
From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction
Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Chaopeng Shen
Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Fearghal O'Donncha
IBM Research, Dublin, Ireland
Yalan Song
Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Wei Zhi
Hohai University, Nanjing, China
Hylke E. Beck
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Tadd Bindas
Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Nicholas Kraabel
Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Kathryn Lawson
Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Viewed
Total article views: 10,530 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Apr 2025)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 8,164 | 2,162 | 204 | 10,530 | 561 | 189 | 228 |
- HTML: 8,164
- PDF: 2,162
- XML: 204
- Total: 10,530
- Supplement: 561
- BibTeX: 189
- EndNote: 228
Total article views: 5,446 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Dec 2025)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,228 | 1,110 | 108 | 5,446 | 221 | 91 | 107 |
- HTML: 4,228
- PDF: 1,110
- XML: 108
- Total: 5,446
- Supplement: 221
- BibTeX: 91
- EndNote: 107
Total article views: 5,084 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Apr 2025)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,936 | 1,052 | 96 | 5,084 | 340 | 98 | 121 |
- HTML: 3,936
- PDF: 1,052
- XML: 96
- Total: 5,084
- Supplement: 340
- BibTeX: 98
- EndNote: 121
Viewed (geographical distribution)
Total article views: 10,530 (including HTML, PDF, and XML)
Thereof 10,271 with geography defined
and 259 with unknown origin.
Total article views: 5,446 (including HTML, PDF, and XML)
Thereof 5,212 with geography defined
and 234 with unknown origin.
Total article views: 5,084 (including HTML, PDF, and XML)
Thereof 5,059 with geography defined
and 25 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
- Scour depth prediction using machine learning and explainable AI: assessment of bridge vulnerability P. Kanishkavardhan et al. https://doi.org/10.3389/fbuil.2026.1790274
- WRO-water: A hydrology-guided multi-head attention transformer for runoff prediction and water quality early warning Q. Abbas et al. https://doi.org/10.1016/j.jenvman.2026.130545
- Leveraging U-Net/CNN-Based Segmentation in Deep-Learning-driven Strain Analysis of Metasedimentary Rocks N. Ismayilova et al. https://doi.org/10.1016/j.geoai.2026.100112
- A prompt-conditioned multimodal framework for streamflow prediction with lightweight task adaptation X. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135915
- Physics-guided transformer-based modeling for surface water quality prediction in aquatic environments Z. Mu et al. https://doi.org/10.1016/j.jwpe.2026.109908
- Stream flow prediction utilizing deep learning models in the Lake Abaya-Chamo sub-basin, South Ethiopia D. Areru et al. https://doi.org/10.1007/s43621-026-03188-8
- Operation rules for the Colônia River reservoir under extreme drought: Dynamic Programming and Artificial Neural Networks T. Silva & M. Cabrera https://doi.org/10.1590/2318-0331.312620250174
- A coupled model for water quality source tracing and prediction in arid industrialized regions M. Liu et al. https://doi.org/10.1016/j.pce.2026.104458
- Spectral-enhanced cross-domain network: a novel daily runoff prediction model based on energy amplification and cross-domain multi-scale collaboration R. Wang & T. Yang https://doi.org/10.1016/j.jhydrol.2026.136018
- Coupling strategies of snowmelt runoff model and machine learning in the Lhasa River Basin T. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103400
- Multi-indicator water-quality prediction in mining areas using a feature-tokenizer transformer with spatiotemporal features Z. Liu et al. https://doi.org/10.1016/j.envres.2026.125210
- A study on the GRU-LSTM hybrid model with dual attention mechanism for reservoir water level forecasting G. Yang & F. Zhou https://doi.org/10.1007/s13201-026-02893-z
- Optimisation of machine learning sediment forecasting models for a typical watershed in the middle reaches of the Yellow River C. Lei et al. https://doi.org/10.2166/wpt.2026.373
- Integrating Smart Meter Data and Building Energy Modeling for Demand Response Management Using Deep Learning and Temporal Database Techniques R. Wang et al. https://doi.org/10.1109/ACCESS.2026.3708301
15 citations as recorded by crossref.
- Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
- Scour depth prediction using machine learning and explainable AI: assessment of bridge vulnerability P. Kanishkavardhan et al. https://doi.org/10.3389/fbuil.2026.1790274
- WRO-water: A hydrology-guided multi-head attention transformer for runoff prediction and water quality early warning Q. Abbas et al. https://doi.org/10.1016/j.jenvman.2026.130545
- Leveraging U-Net/CNN-Based Segmentation in Deep-Learning-driven Strain Analysis of Metasedimentary Rocks N. Ismayilova et al. https://doi.org/10.1016/j.geoai.2026.100112
- A prompt-conditioned multimodal framework for streamflow prediction with lightweight task adaptation X. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135915
- Physics-guided transformer-based modeling for surface water quality prediction in aquatic environments Z. Mu et al. https://doi.org/10.1016/j.jwpe.2026.109908
- Stream flow prediction utilizing deep learning models in the Lake Abaya-Chamo sub-basin, South Ethiopia D. Areru et al. https://doi.org/10.1007/s43621-026-03188-8
- Operation rules for the Colônia River reservoir under extreme drought: Dynamic Programming and Artificial Neural Networks T. Silva & M. Cabrera https://doi.org/10.1590/2318-0331.312620250174
- A coupled model for water quality source tracing and prediction in arid industrialized regions M. Liu et al. https://doi.org/10.1016/j.pce.2026.104458
- Spectral-enhanced cross-domain network: a novel daily runoff prediction model based on energy amplification and cross-domain multi-scale collaboration R. Wang & T. Yang https://doi.org/10.1016/j.jhydrol.2026.136018
- Coupling strategies of snowmelt runoff model and machine learning in the Lhasa River Basin T. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103400
- Multi-indicator water-quality prediction in mining areas using a feature-tokenizer transformer with spatiotemporal features Z. Liu et al. https://doi.org/10.1016/j.envres.2026.125210
- A study on the GRU-LSTM hybrid model with dual attention mechanism for reservoir water level forecasting G. Yang & F. Zhou https://doi.org/10.1007/s13201-026-02893-z
- Optimisation of machine learning sediment forecasting models for a typical watershed in the middle reaches of the Yellow River C. Lei et al. https://doi.org/10.2166/wpt.2026.373
- Integrating Smart Meter Data and Building Energy Modeling for Demand Response Management Using Deep Learning and Temporal Database Techniques R. Wang et al. https://doi.org/10.1109/ACCESS.2026.3708301
Saved (final revised paper)
Latest update: 25 Aug 2026
Editorial statement
Machine learning is used widely in hydrological research nowadays, but benchmarking them for various applications was lacking. This paper addresses the question which machine learning model to be used for which application and why.
Machine learning is used widely in hydrological research nowadays, but benchmarking them for...
Short summary
Using global and regional datasets, we compared attention-based models and Long Short-Term Memory (LSTM) models to predict hydrologic variables. Our results show LSTM models perform better in simpler tasks, whereas attention-based models perform better in complex scenarios, offering insights for improved water resource management.
Using global and regional datasets, we compared attention-based models and Long Short-Term...
