Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-415-2023
https://doi.org/10.5194/nhess-23-415-2023
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

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Cited articles

Balogun, A. L. and Adebisi, N.: Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models' accuracy, Geomat. Nat. Haz. Risk, 12, 653–674, 2021. 
Belmonte Rivas, M. and Stoffelen, A.: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, https://doi.org/10.5194/os-15-831-2019, 2019.  
Bonaduce, A., Staneva, J., Grayek, S., Bidlot, J. R., and Breivik, Ø.: Sea-state contributions to sea-level variability in the European Seas, Ocean Dynamics, 70, 1547–1569, 2020. 
Bruneau, N., Polton, J., Williams, J., and Holt, J.: Estimation of global coastal sea level extremes using neural networks. Environ. Res. Lett., 15, 074030, https://doi.org/10.1088/1748-9326/ab89d6, 2020. 
Climate Data Store (CDS): https://cds.climate.copernicus.eu/, last access: 20 July 2021. 
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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.
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