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-23-415-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-415-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 2
Research article
 | 
02 Feb 2023
Research article |  | 02 Feb 2023

Detecting anomalous sea-level states in North Sea tide gauge data using an autoassociative neural network

Kathrin Wahle, Emil V. Stanev, and Joanna Staneva

Viewed

Total article views: 1,486 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,144 295 47 1,486 32 27
  • HTML: 1,144
  • PDF: 295
  • XML: 47
  • Total: 1,486
  • BibTeX: 32
  • EndNote: 27
Views and downloads (calculated since 29 Jun 2022)
Cumulative views and downloads (calculated since 29 Jun 2022)

Viewed (geographical distribution)

Total article views: 1,486 (including HTML, PDF, and XML) Thereof 1,410 with geography defined and 76 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Jul 2024
Download
Short summary
Knowledge of what causes maximum water levels is often key in coastal management. Processes, such as storm surge and atmospheric forcing, alter the predicted tide. Whilst most of these processes are modeled in present-day ocean forecasting, there is still a need for a better understanding of situations where modeled and observed water levels deviate from each other. Here, we will use machine learning to detect such anomalies within a network of sea-level observations in the North Sea.
Read more
Special issue
Coastal hazards and hydro-meteorological extremes
Altmetrics
Final-revised paper
Preprint