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On the Application of Machine Learning Techniques to Regression Problems in Sea Level Studies

Journal article
Authors M. Hieronymus
J. Hieronymus
Fredrik Hieronymus
Published in Journal of Atmospheric and Oceanic Technology
Volume 36
Issue 9
Pages 1889-1902
ISSN 0739-0572
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Pharmacology
Pages 1889-1902
Language en
Keywords Europe, Neural networks, Time series, Ocean models, Regional models, empirical orthogonal functions, neural-networks, extreme sea, north-sea, model, variability, rise, tide, Engineering, Meteorology & Atmospheric Sciences
Subject categories Meteorology and Atmospheric Sciences


Long sea level records with high temporal resolution are of paramount importance for future coastal protection and adaptation plans. Here we discuss the application of machine learning techniques to some regression problems commonly encountered when analyzing such time series. The performance of artificial neural networks is compared with that of multiple linear regression models on sea level data from the Swedish coast. The neural networks are found to be superior when local sea level forcing is used together with remote sea level forcing and meteorological forcing, whereas the linear models and the neural networks show similar performance when local sea level forcing is excluded. The overall performance of the machine learning algorithms is good, often surpassing that of the much more computationally costly numerical ocean models used at our institute.

Page Manager: Webmaster|Last update: 9/11/2012

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