کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1721145 | 1014469 | 2011 | 10 صفحه PDF | دانلود رایگان |
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.
Journal: Coastal Engineering - Volume 58, Issue 6, June 2011, Pages 453–462