Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 8
Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-3683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 8
Research article
 | 
07 Aug 2026
Research article |  | 07 Aug 2026

Storm surge dynamics in the northern Adriatic Sea: comparing AI emulators with high-resolution numerical simulations

Rodrigo Campos-Caba, Paula Camus, Andrea Mazzino, Michalis Vousdoukas, Massimo Tondello, Ivan Federico, Salvatore Causio, and Lorenzo Mentaschi

Viewed

Total article views: 3,898 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,662 1,030 206 3,898 365 184 176
  • HTML: 2,662
  • PDF: 1,030
  • XML: 206
  • Total: 3,898
  • Supplement: 365
  • BibTeX: 184
  • EndNote: 176
Views and downloads (calculated since 18 Nov 2025)
Cumulative views and downloads (calculated since 18 Nov 2025)

Viewed (geographical distribution)

Total article views: 3,898 (including HTML, PDF, and XML) Thereof 3,854 with geography defined and 44 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Aug 2026
Download
Short summary
We assess the ability of machine learning emulators, from Multivariate Linear Regression to Long Short-Term Memory (LSTM) networks, to reproduce storm surge dynamics in the northern Adriatic Sea. Using the corrected Mean Absolute Deviation squared (MADc²) loss function, we demonstrate that data-driven models can match high-resolution hydrodynamic simulations in representing extreme surge events with greatly reduced computational cost.
Read more
Share
Altmetrics
Final-revised paper
Preprint