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.
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.
the Creative Commons Attribution 4.0 License.
Detecting anomalous sea-level states in North Sea tide gauge data using an autoassociative neural network
Kathrin Wahle
CORRESPONDING AUTHOR
Helmholtz Zentrum Hereon, Geesthacht, Germany
Emil V. Stanev
Helmholtz Zentrum Hereon, Geesthacht, Germany
Research Department, University of Sofia “St. Kliment Ohridski”,
Sofia, Bulgaria
Department of Meteorology and Geophysics, University of Sofia “St.
Kliment Ohridski”, Sofia, Bulgaria
Joanna Staneva
Helmholtz Zentrum Hereon, Geesthacht, Germany
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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.
Knowledge of what causes maximum water levels is often key in coastal management. Processes,...
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