eprintid: 475 rev_number: 7 eprint_status: archive userid: 2 dir: disk0/00/00/04/75 datestamp: 2021-11-25 20:22:15 lastmod: 2023-07-07 23:30:19 status_changed: 2021-11-17 16:18:08 type: article metadata_visibility: show creators_name: García-Alba, Javier creators_name: Bárcena, Javier F. creators_name: Pedraz, Luis creators_name: Fernández, Felipe creators_name: García, Andrés creators_name: Mecías-Calvo, Marcos creators_name: Costas-Veiga, Javier creators_name: Sámano Celorio, María Luisa creators_name: Szpilman, David creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: marcos.mecias@uneatlantico.es creators_id: javier.costas@uneatlantico.es creators_id: marialuisa.samano@uneatlantico.es creators_id: title: SOSeas Web App: An assessment web-based decision support tool to predict dynamic risk of drowning on beaches using deep neural networks. ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica full_text_status: none 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. date: 2021-11 date_type: published publication: Journal of Operational Oceanography pagerange: 1-20 id_number: doi:10.1080/1755876X.2021.1999107 refereed: TRUE issn: 1755-876X official_url: http://doi.org/10.1080/1755876X.2021.1999107 access: close language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Cerrado Inglés 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. metadata García-Alba, Javier; Bárcena, Javier F.; Pedraz, Luis; Fernández, Felipe; García, Andrés; Mecías-Calvo, Marcos; Costas-Veiga, Javier; Sámano Celorio, María Luisa y Szpilman, David mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, marcos.mecias@uneatlantico.es, javier.costas@uneatlantico.es, marialuisa.samano@uneatlantico.es, SIN ESPECIFICAR (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