Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 20
Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5251-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5251-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 20
Research article
 | 
17 Oct 2025
Research article |  | 17 Oct 2025

Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques

Aohan Jin, Wenguang Shi, Jun Du, Renjie Zhou, Hongbin Zhan, Yao Huang, Quanrong Wang, and Xuan Gu

Viewed

Total article views: 3,401 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,616 651 134 3,401 263 134 212
  • HTML: 2,616
  • PDF: 651
  • XML: 134
  • Total: 3,401
  • Supplement: 263
  • BibTeX: 134
  • EndNote: 212
Views and downloads (calculated since 16 Jan 2025)
Cumulative views and downloads (calculated since 16 Jan 2025)

Viewed (geographical distribution)

Total article views: 3,401 (including HTML, PDF, and XML) Thereof 3,388 with geography defined and 13 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 19 May 2026
Download
Short summary
This study developed a novel physics-informed deep transfer learning (PDTL) approach, which integrates the physical mechanisms from an analytical model using a transfer learning technique. Results indicate that the DTL model maintains satisfactory performance even in heterogeneous conditions, with uncertainties in observations and sparse training data compared to the deep neural network (DNN) model.
Read more
Share