Rip Current: A Potential Hazard Zones Detection in Saint Martin’s Island using Deep Learning and Machine Learning Approach

Abstract

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 research suggests an approach for something like the automatic detection of rip currents with waves crashing based on convolutional neural networks (CNNs) and machine learning algorithms (MLAs) for classification. Security cameras can be placed at any elevated position around the beach near the coastguard’s office, and mobile phones from a certain height have still images of something like the shoreline and represent a possible cause of rip current measurements and management to handle this hazard appropriately. This work is about using CNN and MLAs to build detection systems from still beach images, bathymetric images, and beach parameters. The CNN-based detection model for beach images and bathymetric images has already been put into place. MLAs have been applied to detect rip currents based on beach parameters. Compared to other detection models, detection models based on bathymetric images are much more accurate and precise. The VGG16 model of CNN shows a maximum accuracy of 91.13% (recall = 0.94, F1-score = 0.87) for beach images. For the bathymetric images, the best 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 the random forest classifier. Once the potential zone of continuously generating rip current has been identified, the coastal region can be managed accordingly to prevent accidents due to this coastal hazard