Articles | Volume 25, issue 5
https://doi.org/10.5194/nhess-25-1655-2025
https://doi.org/10.5194/nhess-25-1655-2025
Research article
 | 
09 May 2025
Research article |  | 09 May 2025

Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates

Naveen Ragu Ramalingam, Kendra Johnson, Marco Pagani, and Mario L. V. Martina

Viewed

Total article views: 822 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
579 200 43 822 69 39 116
  • HTML: 579
  • PDF: 200
  • XML: 43
  • Total: 822
  • Supplement: 69
  • BibTeX: 39
  • EndNote: 116
Views and downloads (calculated since 13 May 2024)
Cumulative views and downloads (calculated since 13 May 2024)

Viewed (geographical distribution)

Total article views: 822 (including HTML, PDF, and XML) Thereof 802 with geography defined and 20 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 May 2025
Download
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.
Share
Altmetrics
Final-revised paper
Preprint