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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Aug 2026
Research article |
| 07 Aug 2026
| 07 Aug 2026
Storm surge dynamics in the northern Adriatic Sea: comparing AI emulators with high-resolution numerical simulations
Rodrigo Campos-Caba
CORRESPONDING AUTHOR
Department of Physics and Astronomy (DIFA), University of Bologna, 40127 Bologna, Italy
Interdepartmental Research Centre for Environmental Sciences (CIRSA), University of Bologna, 48123 Ravenna, Italy
Paula Camus
Departamento de Matemática Aplicada y Ciencias de la Computación, University of Cantabria, 39005 Santander, Spain
Andrea Mazzino
Department of Civil, Chemical and Environmental Engineering, University of Genoa, 16145 Genoa, Italy
Istituto Nazionale di Fisica Nucleare, Sezione di Genova, 16146 Genoa, Italy
Michalis Vousdoukas
Department of Marine Sciences, University of the Aegean, 81100 Mitilene, Greece
MV Coastal and Climate Research Ltd., 3046 Limassol, Cyprus
Massimo Tondello
HS Marine SrL, 35027 Noventa Padovana, Italy
Ivan Federico
CMCC Foundation – Euro-Mediterranean Center on Climate Change, 73100 Lecce, Italy
Salvatore Causio
CMCC Foundation – Euro-Mediterranean Center on Climate Change, 73100 Lecce, Italy
Lorenzo Mentaschi
CORRESPONDING AUTHOR
Department of Physics and Astronomy (DIFA), University of Bologna, 40127 Bologna, Italy
Interdepartmental Research Centre for Environmental Sciences (CIRSA), University of Bologna, 48123 Ravenna, Italy
CMCC Foundation – Euro-Mediterranean Center on Climate Change, 73100 Lecce, Italy
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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.
We assess the ability of machine learning emulators, from Multivariate Linear Regression to Long...
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