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Analysis of clustering and selection algorithms for the study of multivariate wave climate

Abstract: Recent wave reanalysis databases require the application of techniques capable of managing huge amounts of information. In this paper, several clustering and selection algorithms: K-Means (KMA), self-organizing maps (SOM) and Maximum Dissimilarity (MDA) have been applied to analyze trivariate hourly time series of met-ocean parameters (significant wave height, mean period, and mean wave direction). A methodology has been developed to apply the aforementioned techniques to wave climate analysis, which implies data pre-processing and slight modifications in the algorithms. Results show that: a) the SOM classifies the wave climate in the relevant ?wave types? projected in a bidimensional lattice, providing an easy visualization and probabilistic multidimensional analysis; b) the KMA technique correctly represents the average wave climate and can be used in several coastal applications such as longshore drift or harbor agitation; c) the MDA algorithm allows selecting a representative subset of the wave climate diversity quite suitable to be implemented in a nearshore propagation methodology.

 Autoría: Camus P., Mendez F.J., Medina R., Cofiño A.S.,

 Fuente: Coastal Engineering, 2011, 8(6), 453-462

 Editorial: Elsevier

 Fecha de publicación: 01/06/2011

 Nº de páginas: 10

 Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.coastaleng.2011.02.003

 ISSN: 0378-3839,1872-7379

 Proyecto español: CSD2007-00067

 Url de la publicación: https://doi.org/10.1016/j.coastaleng.2011.02.003