Rip current
Beach hazards would be any occurrences potentially endanger individuals as well as their activity. Rip current, or reverse current of the sea, is a type of wave that pushes against the shore and moves in the opposite direction, that is, towards the deep sea. The management of access to the beach som...
| Autores: | , |
|---|---|
| Tipo de documento: | artigo |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositório: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglês |
| OAI Identifier: | oai:ddd.uab.cat:270469 |
| Acesso em linha: | https://ddd.uab.cat/record/270469 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1604 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Rip Current Convolutional Neural Network (CNN) Machine Learning Algorithms (MLAs) Coastal Hazards Management Beach Management |
| Resumo: | Beach hazards would be any occurrences potentially endanger individuals as well as their activity. Rip current, or reverse current of the sea, is a type of wave that pushes against the shore and moves in the opposite direction, that is, towards the deep sea. The management of access to the beach sometimes accidentally push unwary beachgoers forward into rip-prone regions, increasing the probability of a drowning on that beach. The research suggests an approach for something like the automatic detection of rip currents with waves crashing based on convolutional neural networks (CNN) and machine learning algorithms (MLAs) for classification. Several individuals are unable to identify rip currents in order to prevent them. In addition, the absence of evidence to aid in training and validating hazardous systems hinders attempts to predict rip currents. Security cameras and mobile phones have still images of something like the shore pervasive and represent a possible cause of rip current measurements and management to handle this hazards accordingly. This work deals with developing detection systems from still beach images, bathymetric images, and beach parameters using CNN and MLAs. The detection model based on CNN for the input features of beach images and bathymetric images has been implemented. MLAs have been applied to detect rip currents based on beach parameters. When compared to other detection models, bathymetric image-based detection models have significantly higher accuracy and precision. The VGG16 model of CNN shows maximum accuracy of 91.13% (Recall = 0.94, F1-score = 0.87) for beach images. For the bathymetric images, the highest performance has been found with an accuracy of 96.89% (Recall= 0.97, F1-score=0.92) for the DenseNet model of CNN. The MLA-based model shows an accuracy of 86.98% (Recall=0.89, F1-score= 0.90) for random forest classifier. Once we know about the potential zone of rip current continuosly generating rip current, then the coastal region can be managed accordingly to prevent the accidents occured due to this coastal hazards. |
|---|