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Journal of Korean Society of Coastal and Ocean Engineers > Volume 32(6); 2020 > Article
J Korean Soc Coast Ocean Eng 2020;32(6): 561-568. doi: https://doi.org/10.9765/KSCOE.2020.32.6.561
인공신경망을 이용한 X-Band 레이다 유의파고 추정
박재성1, 안경모2, 오찬영3, 장연식4
Estimation of Significant Wave Heights from X-Band Radar Using Artificial Neural Network
Jaeseong Park1, Kyungmo Ahn2, Chanyeong Oh3, Yeon S. Chang4
1Graduate Student, Dept. of Spatial Design & Eng., Handong Global University
2Professor, School of Spatial Environment System Engineering/Research Institute of Floating Offshore Wind-power Generation Farm Field, Handong Global University
3Associate Research Engineer, Institute of Construction & Environmental Research, Handong Global University
4Principal Research Scientist, Maritime ICT R&D Center, Korea Institute of Ocean Science and Technology
Corresponding author: Kyungmo Ahn ,Tel: +82-54-260-1421, Email: kmahn@handong.edu
Received: December 15, 2020;  Revised: December 22, 2020.  Accepted: December 22, 2020.
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Wave measurements using X-band radar have many advantages compared to other wave gauges including wave-rider buoy, P-u-v gauge and Acoustic Doppler Current Profiler (ADCP), etc.. For example, radar system has no risk of loss/damage in bad weather conditions, low maintenance cost, and provides spatial distribution of waves from deep to shallow water. This paper presents new methods for estimating significant wave heights of X-band marine radar images using Artificial Neural Network (ANN). We compared the time series of estimated significant wave heights (Hs) using various estimation methods, such as signal-to-noise ratio (SNR), both SNR and the peak period (TP), and ANN with 3 parameters (SNR, TP, and Rval > k). The estimated significant wave heights of the X-band images were compared with wave measurement using ADCP(AWC: Acoustic Wave and Current Profiler) at Hujeong Beach, Uljin, Korea. Estimation of Hs using ANN with 3 parameters (SNR, TP, and Rval > k) yields best result.
Keywords: X-band radar, significant wave heights, machine learning, artificial neural network (ANN), peak period
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