García-Alba, Javier and Bárcena, Javier F. and Pedraz, Luis and Fernández, Felipe and García, Andrés and Mecías-Calvo, Marcos and Costas-Veiga, Javier and Sámano Celorio, María Luisa and Szpilman, David UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, marcos.mecias@uneatlantico.es, javier.costas@uneatlantico.es, marialuisa.samano@uneatlantico.es, UNSPECIFIED (2021) SOSeas Web App: An assessment web-based decision support tool to predict dynamic risk of drowning on beaches using deep neural networks. Journal of Operational Oceanography. pp. 1-20. ISSN 1755-876X
Full text not available from this repository.Abstract
People still drown on beaches in unacceptable numbers due to the lack of knowledge about the risks taking place in them. The proposed methodology forecasts electronic bathing flags in beaches by integrating the benefits of metocean operational systems, machine learning and web-based decision support technologies into a 24/7 risk assessment service that could be easily implemented at any beach worldwide with low costs of maintenance. Firstly, a crosscutting analysis between metocean conditions, beach characteristics and flag records was performed. Secondly, an expert system, based on Deep Learning, was developed to obtain electronic bathing flags as an indicator of the dynamic risk of drowning on beaches. The input variables of the Deep Neural Network were significant wave height, mean wave period, wind velocity, marine current velocity, incidence angle, and beach modal state. Finally, the application of the method to the Santa Catarina’s beaches (Brazil) conveniently reproduced the status flag of beaches.
| Item Type: | Article |
|---|---|
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 24 Nov 2021 14:12 |
| Last Modified: | 07 Jul 2023 11:38 |
| URI: | http://repositorio.funiber.org/id/eprint/475 |
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