期刊
COASTAL ENGINEERING
卷 58, 期 6, 页码 453-462出版社
ELSEVIER
DOI: 10.1016/j.coastaleng.2011.02.003
关键词
Data mining; K-means; Maximum dissimilarity algorithm; Probability density function; Reanalysis database; Self-organizing maps
资金
- Spanish Ministry MICIN [CSD2007-00067]
- Spanish Ministry MF
- Spanish Ministry MAMRM
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 metocean 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 preprocessing 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. (C) 2011 Elsevier B.V. All rights reserved.
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