Rip current : a potential hazard zones detection in Saint Martin's Island using Machine Learning Approach
Islam, Md. Ariful (University of Dhaka. Department of Robotics and Mechatronics Engineering)
Shampa, Mosa. Tania Alim (University of Dhaka. Department of Oceanography)

Date:	2022
Abstract:	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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Rip Current ; Convolutional Neural Network (CNN) ; Machine Learning Algorithms (MLAs) ; Coastal Hazards Management ; Beach Management
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 21 Núm. 2 (2022) , p. 63-81 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1604
DOI: 10.5565/rev/elcvia.1604

19 p, 17.9 MB

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