气候变化领域集成服务门户(CCPortal): Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images

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DOI	10.3390/jmse12010172
	Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images
	Yoo, Ho-Jun; Kim, Hyoseob; Kang, Tae-Soon; Kim, Ki-Hyun; Bang, Ki-Young; Kim, Jong-Beom; Park, Moon-Sang
发表日期	2024
EISSN	2077-1312
起始页码	12
结束页码	1
卷号	12 期号:1
英文摘要	Coastal erosion is caused by various factors, such as harbor development along coastal areas and climate change. Erosion has been accelerated recently due to sea level rises, increased occurrence of swells, and higher-power storm waves. Proper understanding of the complex coastal erosion process is vital to prepare measures when they are needed. Monitoring systems have been widely established around a high portion of the Korean coastline, supported by several levels of governments, but valid analysis of the collected data and the following preparation of measures have not been highly effective yet. In this paper, we use a drone to obtain bed material images, and an analysis system to predict the representative grain size of beach sands from the images based on artificial intelligence (AI) analysis. The predicted grain sizes are verified via field samplings. Field bed material samples for the particle size analysis are collected during two seasons, while a drone takes photo images and the exact positions are simultaneously measured at Jangsa beach, Republic of Korea. The learning and testing results of the AI technology are considered satisfactory. Finally, they are used to diagnose the overall stability of Jangsa beach. A beach diagnostic grade is proposed here, which reflects the topography of a beach and the distribution of sediments on the beach. The developed beach diagnostic grade could be used as an indicator of any beach stability on the east coast of the Republic of Korea. When the diagnostic grade changes rapidly at a beach, it is required to undergo thorough investigation to understand the reason and foresee the future of the beach conditions, if we want the beach to function as well as before.
英文关键词	estimate of sand particle size based on AI; nonlinear subtraction kernel neural network (SNN); analysis of beach stability; Jangsa beach
语种	英语
WOS研究方向	Engineering ; Oceanography
WOS类目	Engineering, Marine ; Engineering, Ocean ; Oceanography
WOS记录号	WOS:001151404400001
来源期刊	JOURNAL OF MARINE SCIENCE AND ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302519
作者单位	Kookmin University
推荐引用方式 GB/T 7714	Yoo, Ho-Jun,Kim, Hyoseob,Kang, Tae-Soon,et al. Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images[J],2024,12(1).
APA	Yoo, Ho-Jun.,Kim, Hyoseob.,Kang, Tae-Soon.,Kim, Ki-Hyun.,Bang, Ki-Young.,...&Park, Moon-Sang.(2024).Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images.JOURNAL OF MARINE SCIENCE AND ENGINEERING,12(1).
MLA	Yoo, Ho-Jun,et al."Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images".JOURNAL OF MARINE SCIENCE AND ENGINEERING 12.1(2024).