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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Knowledge of what causes maximum water levels is often key in coastal management. Processes,...
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