Articles | Volume 25, issue 5
https://doi.org/10.5194/nhess-25-1655-2025
© 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/nhess-25-1655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates
Naveen Ragu Ramalingam
CORRESPONDING AUTHOR
University School for Advanced Studies – IUSS Pavia, Pavia, 27100, Italy
Kendra Johnson
Global Earthquake Model (GEM) Foundation, Pavia, 27100, Italy
Marco Pagani
Global Earthquake Model (GEM) Foundation, Pavia, 27100, Italy
Institute of Catastrophe Risk Management, Nanyang Technological University, 639798, Singapore
Mario L. V. Martina
University School for Advanced Studies – IUSS Pavia, Pavia, 27100, Italy
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Short summary
By combining limited tsunami simulations with machine learning, we developed a fast and efficient framework to predict tsunami impacts such as wave heights and inundation depths at different coastal sites. Testing our model with historical tsunami source scenarios helped assess its reliability and broad applicability. This work enables more efficient and comprehensive tsunami hazard modelling workflow, which is essential for tsunami risk evaluations and enhancing coastal disaster preparedness.
By combining limited tsunami simulations with machine learning, we developed a fast and...
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