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On the Application of Machine Learning Techniques to Regression Problems in Sea Level Studies
- Hieronymus, M. (author)
- Hieronymus, J. (author)
-
- Hieronymus, Fredrik, 1986 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för farmakologi,Institute of Neuroscience and Physiology, Department of Pharmacology
- (creator_code:org_t)
- American Meteorological Society, 2019
- 2019
- English.
- In: Journal of Atmospheric and Oceanic Technology. - : American Meteorological Society. - 0739-0572 .- 1520-0426. ; 36:9, s. 1889-1902
- Journal article (peer-reviewed)
Abstract
Subject headings
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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.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Meteorologi och atmosfärforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Meteorology and Atmospheric Sciences (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- art (subject category)
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