Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 23, issue 2
Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-809-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-809-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 2
Research article
 | 
24 Feb 2023
Research article |  | 24 Feb 2023

Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany

Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann

Viewed

Total article views: 3,805 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,820 907 78 3,805 121 109 125
  • HTML: 2,820
  • PDF: 907
  • XML: 78
  • Total: 3,805
  • Supplement: 121
  • BibTeX: 109
  • EndNote: 125
Views and downloads (calculated since 03 Nov 2022)
Cumulative views and downloads (calculated since 03 Nov 2022)

Viewed (geographical distribution)

Total article views: 3,805 (including HTML, PDF, and XML) Thereof 3,713 with geography defined and 92 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Read more
Share
Special issue
Advances in pluvial and fluvial flood forecasting and assessment...
Altmetrics
Final-revised paper
Preprint