Wave extreme characterization using self-organizing maps
The self-organizing map (SOM) technique is considered and extended to assess the extremes of a multivariate sea wave climate at a site. The main purpose is to obtain a more complete representation of the sea states, including the most severe states that otherwise would be missed by a SOM. Indeed, it...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | Ocean Science |
| Online Access: | http://www.ocean-sci.net/12/403/2016/os-12-403-2016.pdf |
| _version_ | 1818308207606497280 |
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| author | F. Barbariol F. M. Falcieri C. Scotton A. Benetazzo S. Carniel M. Sclavo |
| author_facet | F. Barbariol F. M. Falcieri C. Scotton A. Benetazzo S. Carniel M. Sclavo |
| author_sort | F. Barbariol |
| collection | DOAJ |
| description | The self-organizing map (SOM) technique is considered and extended to assess
the extremes of a multivariate sea wave climate at a site. The main purpose is to
obtain a more complete representation of the sea states, including the most
severe states that otherwise would be missed by a SOM. Indeed, it is commonly
recognized, and herein confirmed, that a SOM is a good regressor of a sample if
the frequency of events is high (e.g., for low/moderate sea states), while a SOM
fails if the frequency is low (e.g., for the most severe sea states).
Therefore, we have considered a trivariate wave climate (composed by
significant wave height, mean wave period and mean wave direction) collected
continuously at the Acqua Alta oceanographic tower (northern Adriatic
Sea, Italy) during the period 1979–2008. Three different strategies derived
by SOM have been tested in order to capture the most extreme events. The
first contemplates a pre-processing of the input data set aimed at reducing
redundancies; the second, based on the post-processing of SOM outputs,
consists in a two-step SOM where the first step is applied to the original
data set, and the second step is applied on the events exceeding a given
threshold. A complete graphical representation of the outcomes of a two-step
SOM is proposed. Results suggest that the post-processing strategy is more
effective than the pre-processing one in order to represent the wave climate
extremes. An application of the proposed two-step approach is also provided,
showing that a proper representation of the extreme wave climate leads to
enhanced quantification of, for instance, the alongshore component of the
wave energy flux in shallow water. Finally, the third strategy focuses on the
peaks of the storms. |
| first_indexed | 2024-12-13T07:10:36Z |
| format | Article |
| id | doaj.art-c8bc39e5e1cd4311a82628f88bf9d732 |
| institution | Directory Open Access Journal |
| issn | 1812-0784 1812-0792 |
| language | English |
| last_indexed | 2024-12-13T07:10:36Z |
| publishDate | 2016-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Ocean Science |
| spelling | doaj.art-c8bc39e5e1cd4311a82628f88bf9d7322022-12-21T23:55:40ZengCopernicus PublicationsOcean Science1812-07841812-07922016-03-0112240341510.5194/os-12-403-2016Wave extreme characterization using self-organizing mapsF. Barbariol0F. M. Falcieri1C. Scotton2A. Benetazzo3S. Carniel4M. Sclavo5Institute of Marine Sciences, Italian National Research Council, Venice, ItalyInstitute of Marine Sciences, Italian National Research Council, Venice, ItalyUniversity of Padua, Padua, ItalyInstitute of Marine Sciences, Italian National Research Council, Venice, ItalyInstitute of Marine Sciences, Italian National Research Council, Venice, ItalyInstitute of Marine Sciences, Italian National Research Council, Venice, ItalyThe self-organizing map (SOM) technique is considered and extended to assess the extremes of a multivariate sea wave climate at a site. The main purpose is to obtain a more complete representation of the sea states, including the most severe states that otherwise would be missed by a SOM. Indeed, it is commonly recognized, and herein confirmed, that a SOM is a good regressor of a sample if the frequency of events is high (e.g., for low/moderate sea states), while a SOM fails if the frequency is low (e.g., for the most severe sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979–2008. Three different strategies derived by SOM have been tested in order to capture the most extreme events. The first contemplates a pre-processing of the input data set aimed at reducing redundancies; the second, based on the post-processing of SOM outputs, consists in a two-step SOM where the first step is applied to the original data set, and the second step is applied on the events exceeding a given threshold. A complete graphical representation of the outcomes of a two-step SOM is proposed. Results suggest that the post-processing strategy is more effective than the pre-processing one in order to represent the wave climate extremes. An application of the proposed two-step approach is also provided, showing that a proper representation of the extreme wave climate leads to enhanced quantification of, for instance, the alongshore component of the wave energy flux in shallow water. Finally, the third strategy focuses on the peaks of the storms.http://www.ocean-sci.net/12/403/2016/os-12-403-2016.pdf |
| spellingShingle | F. Barbariol F. M. Falcieri C. Scotton A. Benetazzo S. Carniel M. Sclavo Wave extreme characterization using self-organizing maps Ocean Science |
| title | Wave extreme characterization using self-organizing maps |
| title_full | Wave extreme characterization using self-organizing maps |
| title_fullStr | Wave extreme characterization using self-organizing maps |
| title_full_unstemmed | Wave extreme characterization using self-organizing maps |
| title_short | Wave extreme characterization using self-organizing maps |
| title_sort | wave extreme characterization using self organizing maps |
| url | http://www.ocean-sci.net/12/403/2016/os-12-403-2016.pdf |
| work_keys_str_mv | AT fbarbariol waveextremecharacterizationusingselforganizingmaps AT fmfalcieri waveextremecharacterizationusingselforganizingmaps AT cscotton waveextremecharacterizationusingselforganizingmaps AT abenetazzo waveextremecharacterizationusingselforganizingmaps AT scarniel waveextremecharacterizationusingselforganizingmaps AT msclavo waveextremecharacterizationusingselforganizingmaps |