SOSeas Web App: An assessment web-based decision support tool to predict dynamic risk of drowning on beaches using deep neural networks

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.

Acknowledgements

Authors are grateful to the useful and valuable contributions provided by the IHCantabria’s staff: Marco Antonio Vega, David Coterillo, David del Prado, Mauricio González, Omar Gutierrez, and Verónica Canovas.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The European Comission under Copernicus Marine Environment Monitoring Service (CMEMS) User Uptake programme funded this study by means of the contract ‘110-DEM5-L10’.