한국해안·해양공학회논문집 (Journal of Korean Society of Coastal and Ocean Engineers)

  • 제36권2호
  • /
  • Pages.37-49
  • /
  • 2024
  • /
  • 1976-8192(pISSN)
  • /
  • 2288-2227(eISSN)
한국해안·해양공학회 (Korean Society of Coastal and Ocean Engineers)
권호 표지
DOI QR코드

DOI QR Code

선형기계학습모델을 이용한 자갈해빈상에서의 쇄파지표 예측

A Study on the Predictions of Wave Breaker Index in a Gravel Beach Using Linear Machine Learning Model

  • 안을혁 (해양수산부 부산항건설사무소) ;
  • 이영찬 (한국해양대학교대학원 토목환경공학과) ;
  • 김도삼 (한국해양대학교 토목공학과) ;
  • 이광호 (한국해양대학교 토목공학과)
  • Eul-Hyuk Ahn (Busan Port Construction Office, Ministry of Oceans and Fisheries) ;
  • Young-Chan Lee (Dept. of Civil Engineering, Korea Maritime and Ocean University) ;
  • Do-Sam Kim (Dept. of Civil Engineering, Korea Maritime and Ocean University) ;
  • Kwang-Ho Lee (Dept. of Civil Engineering, Korea Maritime and Ocean University)
  • 투고 : 2024.03.14
  • 심사 : 2024.03.25
  • 발행 : 2024.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

지금까지 쇄파는 발생기구의 본질적인 복잡성으로 인해 실내수리모형실험을 통해 쇄파파고 및 쇄파수심 등의 쇄파지표 예측을 위한 많은 경험식이 제안되어 왔다. 하지만, 자갈해빈에 대한 쇄파의 특성 및 쇄파지표예측을 위한 연구는 거의 수행되어 있지 않았다. 본 연구에서는 자갈해빈을 대상으로 쇄파파고 및 쇄파수심의 예측을 위하여 회귀 또는 분류 문제와 관련된 다양한 연구 분야에서 높은 예측 성능을 보이는 대표적인 선형기반 기계학습기법에 기반한 쇄파지표를 예측하고자 하였다. 먼저, 자갈해빈에 대하여 기존에 제안된 쇄파지표의 경험식의 적용성을 검토하고 기존의 경험식의 자갈해빈의 쇄파지표 예측성능의 한계성을 극복하기 위하여 다양한 선형기반 기계학습 알고리즘을 적용하여 쇄파지표 예측모델을 구축하였다. 구축된 기계학습모델 중 자갈해빈에서 발생하는 쇄파파고 및 쇄파수심에 대한 높은 예측성능을 보인 모델을 기반으로 손쉬운 계산이 가능한 쇄파지표에 대한 새로운 산정식을 제안하였고 수리모형실험결과 및 기존의 경험식과 비교하고 새롭게 제안한 쇄파지표의 예측성능을 검증하였다. 본 연구에서 제안한 쇄파지표에 대한 경험식은 단순한 다항식임에도 불구하고 자갈해빈에 대한 양호한 예측성능을 보였다.

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

키워드

과제정보

본 연구는 2021학년도 한국해양대학교 신진교수 정착연구 지원사업 연구비 및 해양수산과학기술진흥원(KIMST)의 "월파 정량 관측 기술 개발(20220180)" 사업의 지원을 받아 수행된 연구이며, 연구비 지원에 감사드립니다.

참고문헌

  1. Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281-305. 
  2. Camenen, B. and Larson, M. (2007). Predictive formulas for breaker depth index and breaker type. Journal of Coastal Research, 73(8), 1028-1041. 
  3. Goda, Y. (1970). A synthesis of breaker indices. Transactions of the Japan-142-Society of Civil Engineers, 2(2), 39-40. 
  4. Goda, Y. (2010). Reanalysis of regular and random breaking wave statistics. Coastal Engineering Journal, 52(1), 71-106. 
  5. Iversen, H.W. (1951). Laboratory study of breakers gravity waves. Circular 521, Paper 3, National Bureau of Standards, Washington, D.C., 9-32. 
  6. Kim, D.H., Kim, Y.J., Hur, D.S., Jeon, H.S. and Lee, C. (2010). Calculating expected damage of breakwater using artificial neural network for wave height calculation. Journal of Korean Society of Coastal and Ocean Engineers, 22(2), 126-132 (in Korean). 
  7. Kim, S.-W. and Suh, K.-D. (2011). Prediction of stability number for tetrapod armour block using artificial neural network and M5' model tree. Journal of Korean Society of Coastal and Ocean Engineers, 23(1), 109-117 (in Korean). 
  8. Koh, Y.Y., Park, Y.A. and Choi, K.W. (1993). Sorting and abrasion processes on gravel beach of Jeongdo-ri, Wando, Korea. The Korean Journal of Quaternary Research, 7, 27-40. 
  9. Komar, P.D. and Gaughan, M.K. (1972). Airy wave theory and breaker height prediction. Proceedings of 13th Conference on Coastal Engineering, Vancouver, Canada, 405-418. 
  10. Lara, J.L., Losada, I.J. and Liu, P.L.F. (2006). Breaking waves over a mild gravel slope: Experimental and numerical analysis. Journal of Geophysical Research: Oceans, 111, Issue C11. 
  11. Le Mehaute, B. and Koh, R.C.Y. (1967). On the breaking of waves arriving at an angle to the shore. Journal of Hydraulic Research, 5(1), 67-88. 
  12. Lee, K.H., Kim, T.G. and Kim, D.S. (2020). Prediction of wave breaking using machine learning open source platform. Journal of Korean Society of Coastal and Ocean Engineers, 32(4), 262-272 (in Korean). 
  13. Lee, K.H., Mizutani, N., Hur, D.S. and Kamiya, A. (2007). The effect of groundwater on topographic changes in a gravel beach. Ocean Engineering, 34, 605-615. 
  14. Liu, Y., Niu, X. and Yu, X. (2011). A new predictive formula for inception of regular wave breaking. Coastal Engineering, 58, 877-889. 
  15. McCowan, J. (1984). On the highest wave of permanent type. Philosophical Magazine, 38(5), 351-358. 
  16. Miche, R. (1944). Mouvements ondulatoires de la mer en profondeur constante ou decroissante. Annles de Ponts et Chaussees, 19, 370-406. 
  17. Munk, W.H. (1949). The solitary wave theory and its applications to surf problems. Annals of the New York Academy of Sciences, 51(3), 376-462. 
  18. Ostendorf, D.W. and Madsen, O.S. (1979). An analysis of longshore current and associated sediment transport in the surf zone. Report No. 241, Department of Civil Engineering, MIT, 1165-1178. 
  19. Park, S., Kim, B.K. and Kim, K. (2022). Prediction in dissolved oxygen concentration and occurrence of hypoxia water mass in Jinhae bay based on machine learning model. Journal of Korean Society of Coastal and Ocean Engineers, 34(3), 47-57 (in Korean). 
  20. Rattanapitikon, W., Vivattanasirisak, T. and Shibayama, T. (2003). A proposal of new breaker height formula. Coastal Engineering Journal, 45, 28-48. 
  21. Rattanapitikon, W. and Shibayama, T. (2006). Braking wave formulas for breaking depth and orbital to phase velocity ratio. Coastal Engineering Journal, 48(4), 395-416. 
  22. Samuel, A.L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229. 
  23. Stokes, G.G. (1880). Appendices and supplement to a paper on the theory of oscillatory waves. Mathematical and Physical Papers, 1, 219-229. 
  24. Sunamura, T. and Horikawa, K. (1974). Two-dimensional beach transformation due to waves. Proceedings of 13th Conference on Coastal Engineering, Copenhagen, Denmark, 920-938. 
  25. Xie, W., Shibayama, T. and Esteban, M. (2019). A semi-empirical formula for calculating the breaking depth of plunging waves. Coastal Engineering Journal, 61(2), 199-209.