Journal
JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
Volume 36, Issue 9, Pages 1889-1902Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JTECH-D-19-0033.1
Keywords
Europe; Neural networks; Time series; Ocean models; Regional models
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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.
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