离岸流:利用机器学习方法检测圣马丁岛的潜在危险区

Q4 Computer Science
Md Ariful Islam, Mosa. Tania Alim Shampa
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引用次数: 0

摘要

海滩危害是指任何可能危及个人及其活动的事件。离岸流,或逆流,是一种波浪,它推动海岸,并向相反的方向移动,即向深海移动。进入海滩的管理有时会意外地将粗心的海滩游客推向易撕裂的区域,增加了在海滩上溺水的可能性。该研究提出了一种基于卷积神经网络(CNN)和机器学习算法(mla)进行分类的方法,可以自动检测波浪撞击的离岸流。有几个人无法识别离岸流以防止它们。此外,缺乏有助于培训和验证危险系统的证据阻碍了预测离岸流的尝试。安全摄像头和移动电话都有类似海岸的静态图像,这代表了离岸流测量和管理的可能原因,以相应地处理这种危险。这项工作涉及使用CNN和mla从静止海滩图像、水深图像和海滩参数开发检测系统。实现了基于CNN的海滩图像和水深图像输入特征的检测模型。mla已应用于基于海滩参数的离岸流检测。与其他检测模型相比,基于水深图像的检测模型具有更高的精度和精度。CNN的VGG16模型对海滩图像的最高准确率为91.13% (Recall = 0.94, F1-score = 0.87)。对于水深图像,CNN的DenseNet模型的准确率最高,达到96.89% (Recall= 0.97, F1-score=0.92)。基于mla的模型对随机森林分类器的准确率为86.98% (Recall=0.89,F1-score= 0.90)。一旦我们知道了离岸流持续产生离岸流的潜在区域,那么沿海地区就可以进行相应的管理,以防止由于这种沿海危害而发生的事故。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rip Current: A Potential Hazard Zones Detection in Saint Martin’s Island using Machine Learning Approach
Beach hazards would be any occurrences potentially endanger individuals aswell as their activity. Rip current, or reverse current of the sea, is a typeof 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 suggestsan approach for something like the automatic detection of rip currents withwaves crashing based on convolutional neural networks (CNN) and machinelearning algorithms (MLAs) for classification. Several individuals are unableto identify rip currents in order to prevent them. In addition, the absenceof evidence to aid in training and validating hazardous systems hinders attempts to predict rip currents. Security cameras and mobile phones have stillimages of something like the shore pervasive and represent a possible causeof rip current measurements and management to handle this hazards accordingly. This work deals with developing detection systems from still beachimages, bathymetric images, and beach parameters using CNN and MLAs.The detection model based on CNN for the input features of beach imagesand bathymetric images has been implemented. MLAs have been applied todetect rip currents based on beach parameters. When compared to other detection models, bathymetric image-based detection models have significantlyhigher accuracy and precision. The VGG16 model of CNN shows maximumaccuracy of 91.13% (Recall = 0.94, F1-score = 0.87) for beach images. Forthe bathymetric images, the highest performance has been found with anaccuracy 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 coastalregion can be managed accordingly to prevent the accidents occured due tothis coastal hazards.
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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