Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/1/172 |
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| author | Ho-Jun Yoo Hyoseob Kim Tae-Soon Kang Ki-Hyun Kim Ki-Young Bang Jong-Beom Kim Moon-Sang Park |
| author_facet | Ho-Jun Yoo Hyoseob Kim Tae-Soon Kang Ki-Hyun Kim Ki-Young Bang Jong-Beom Kim Moon-Sang Park |
| author_sort | Ho-Jun Yoo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:44:57Z |
| format | Article |
| id | doaj.art-0daed5c935d4444cac9a1b58b6241353 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T10:44:57Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-0daed5c935d4444cac9a1b58b62413532024-01-26T17:17:38ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-01-0112117210.3390/jmse12010172Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone ImagesHo-Jun Yoo0Hyoseob Kim1Tae-Soon Kang2Ki-Hyun Kim3Ki-Young Bang4Jong-Beom Kim5Moon-Sang Park6Department of Coastal Management, Geosystem Research Corp., Gunpo 15807, Republic of KoreaDepartment of Civil Engineering, Kookmin University, Seoul 02707, Republic of KoreaDepartment of Coastal Management, Geosystem Research Corp., Gunpo 15807, Republic of KoreaDepartment of Coastal Management, Geosystem Research Corp., Gunpo 15807, Republic of KoreaNumerical Model Research Institute, Geosystem Research Corp., Gunpo 15807, Republic of KoreaDepartment of Coastal Management, Geosystem Research Corp., Gunpo 15807, Republic of KoreaDepartment of Geospatial Information, Geosystem Research Corp., Gunpo 15807, Republic of KoreaCoastal 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.https://www.mdpi.com/2077-1312/12/1/172estimate of sand particle size based on AInonlinear subtraction kernel neural network (SNN)analysis of beach stabilityJangsa beach |
| spellingShingle | Ho-Jun Yoo Hyoseob Kim Tae-Soon Kang Ki-Hyun Kim Ki-Young Bang Jong-Beom Kim Moon-Sang Park Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images Journal of Marine Science and Engineering estimate of sand particle size based on AI nonlinear subtraction kernel neural network (SNN) analysis of beach stability Jangsa beach |
| title | Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images |
| title_full | Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images |
| title_fullStr | Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images |
| title_full_unstemmed | Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images |
| title_short | Prediction of Beach Sand Particle Size Based on Artificial Intelligence Technology Using Low-Altitude Drone Images |
| title_sort | prediction of beach sand particle size based on artificial intelligence technology using low altitude drone images |
| topic | estimate of sand particle size based on AI nonlinear subtraction kernel neural network (SNN) analysis of beach stability Jangsa beach |
| url | https://www.mdpi.com/2077-1312/12/1/172 |
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