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On the Application of Machine Learning Techniques to Regression Problems in Sea Level Studies
SMHI, Research Department, Oceanography.
SMHI, Research Department, Oceanography.
2019 (English)In: Journal of Atmospheric and Oceanic Technology, ISSN 0739-0572, E-ISSN 1520-0426, Vol. 36, no 9, p. 1889-1902Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 36, no 9, p. 1889-1902
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
URN: urn:nbn:se:smhi:diva-5442DOI: 10.1175/JTECH-D-19-0033.1ISI: 000486481100001OAI: oai:DiVA.org:smhi-5442DiVA, id: diva2:1360853
Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2019-10-14Bibliographically approved

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Hieronymus, MagnusHieronymus, Jenny

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • de-DE
  • en-GB
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