Morphological classification of microtidal sand and gravel beaches


Autoria(s): López Úbeda, Isabel; Aragonés Pomares, Luis; Villacampa, Yolanda; Compañ, Patricia; Satorre Cuerda, Rosana
Contribuinte(s)

Universidad de Alicante. Departamento de Ingeniería Civil

Universidad de Alicante. Departamento de Matemática Aplicada

Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial

Ingeniería del Terreno y sus Estructuras (InTerEs)

Modelización Matemática de Sistemas

Informática Industrial e Inteligencia Artificial

Data(s)

04/03/2016

04/03/2016

15/11/2015

Resumo

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

Identificador

Ocean Engineering. 2015, 109: 309-319. doi:10.1016/j.oceaneng.2015.09.021

0029-8018 (Print)

1873-5258 (Online)

http://hdl.handle.net/10045/53513

10.1016/j.oceaneng.2015.09.021

Idioma(s)

eng

Publicador

Elsevier

Relação

http://dx.doi.org/10.1016/j.oceaneng.2015.09.021

Direitos

© 2015 Elsevier Ltd.

info:eu-repo/semantics/restrictedAccess

Palavras-Chave #Beaches classification #Artificial neural networks #SVM method #Posidonia oceanica #Ingeniería e Infraestructura de los Transportes #Matemática Aplicada #Ciencia de la Computación e Inteligencia Artificial
Tipo

info:eu-repo/semantics/article