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A methodology for the classification of gravel beaches
Villacampa, Y.; Aragones, L.; Serra, J.F.; Navarro-González, F.J.; López, I. (2015). A methodology for the classification of gravel beaches, in: Rodriguez, G.R. et al. Coastal cities and their sustainable future. WIT Transactions on The Built Environment, 148: pp. 297-306. https://dx.doi.org/10.2495/cc150251
In: Rodriguez, G.R.; Brebbia, C.A. (Ed.) (2015). Coastal cities and their sustainable future. WIT Transactions on The Built Environment, 148. WIT Press: Southampton. ISBN 978-1-84564-9104. 331 pp.
In: WIT Transactions on The Built Environment. WIT Press: Southampton. ISSN 1743-3509, meer
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| Author keywords |
mathematical model; gravel beaches |
| Auteurs | | Top |
- Villacampa, Y.
- Aragones, L.
- Serra, J.F.
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- Navarro-González, F.J.
- López, I.
|
|
| Abstract |
Beaches are highly flexible structures that can be deformed by several reasons, some natural as wind and swell and others not, as human actions. Gravel, considered as a component of the beach is not always separated from the rest of the materials. It is a part of the coastline sedimentary balance, usually with time and spatial scales much greater than those corresponding to the stretch of the coast under study. The conceptual and experimental difficulties of studying this kind of beach have meant that nowadays they are really unknown. In this paper, methodologies to classify and determinate the most important characteristics in gravel beaches are presented. The authors have studied 34 shingle beaches in the region of Alicante (Spain) from a database with their characteristics. Obtained data corresponds to the morphology of the beach, the materials that take part in its composition and the wave energy, considering its incidence, the wave height, the local period and its influence on the coastline. At the beginning, mathematical models are generated, allowing the expression of the relationships between the slope of berm and the rest of variables. To classify the beaches, a factor analysis has been used on the experimental data matrix, considering all the variables as predictive, obtaining in this way an index for beach classification with similar characteristics. Furthermore, to determine the predictive variables that allow characterizing the 34 beaches, a discriminant analysis has been applied over several sets of variables. In each case, a predictive model of cluster belonging is created, considering a discriminant function, and with the clustering function formed by different clusters. The methodologies developed in this paper will be applied later to other beaches as classification and variable selection methods. |
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