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

Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회)
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A Proposal of New Breaker Index Formula Using Supervised Machine Learning

지도학습을 이용한 새로운 선형 쇄파지표식 개발

  • Choi, Byung-Jong (Dept. of Energy and Environmental Eng., Graduate School, Catholic Kwandong University) ;
  • Park, Chang-Wook (OCEANIC C&T Co., Ltd.) ;
  • Cho, Yong-Hwan (Dept. of Civil and Environmental Eng., Nagoya University) ;
  • Kim, Do-Sam (Dept. of Civil Eng., Korea Maritime and Ocean University) ;
  • Lee, Kwang-Ho (Dept. of Civil Engineering, Catholic Kwandong University)
  • 최병종 (가톨릭관동대학교 대학원 에너지환경융합학과) ;
  • 박창욱 ((주) 오셔닉) ;
  • 조용환 (일본 나고야대학교 토목환경공학과) ;
  • 김도삼 (한국해양대학교 건설공학과) ;
  • 이광호 (가톨릭관동대학교 토목공학과)
  • Received : 2020.09.16
  • Accepted : 2020.11.10
  • Published : 2020.12.31
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Abstract

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

연안에서 천수변형에 의해 발생하는 쇄파는 표사이동, 연안류의 생성, 충격파압의 발생 등과 같은 연안역의 다양한 물리현상과 밀접한 관계를 갖고 있다. 따라서, 연안구조물의 설계 시 쇄파파고 및 쇄파수심과 같은 쇄파지표를 정확하게 예측하는 것이 중요하다. 과거부터 많은 연구자들에 의해 쇄파현상을 규명하고 예측하기 위한 많은 과학적인 노력들이 이루어져 왔다. 대표적인 쇄파에 연구들은 주로 수리모형실험을 통해 쇄파지표 예측을 위한 많은 경험식이 제안되어 왔다. 하지만, 기존의 쇄파지표에 대한 경험식은 일정한 방정식의 가정하에 자료의 통계적 분석을 통해 가정한 방정식의 계수들을 결정하고 있다. 본 논문에서는 회귀 혹은 분류문제와 관련된 다양한 연구분야에 있어서 높은 예측성능을 보여주는 대표적인 선형기반의 지도학습 머신러닝 기법을 적용하였다. 적용된 머신러닝 기법을 기반으로 기존의 쇄파에 대한 실험자료로부터 쇄파지표 예측을 위한 모델을 개발하고, 학습된 모델로부터 쇄파예측을 위한 새로운 선형식을 제시하였다. 새롭게 제안된 쇄파지표식은 단순한 선형식임에도 불구하고 기존의 경험 공식에 비해 유사한 예측성능을 보였다.

Keywords

References

  1. Arlot, S. and Celisse, A. (2010). A survey of cross-validation procedures for model selection. Static Surveys, 4. 40-79.
  2. Bataineh, M. and Marler, T. (2017). Neural network for regression problems with reduced training sets. Neural Networks, 95, 1-9. https://doi.org/10.1016/j.neunet.2017.07.018
  3. Bergstra, J. and Bengio, Y. (2012). Random search for hyperparameter optimization. Journal of Machine Learning Research, 13, 281-305.
  4. Buscombe, D., Carini, R.J., Harrison, S.R., Chickadel, C.C. and Warrick, J.A. (2020). Optical wave gauging using deep neural networks. Coastal Engineering, 155, 103593. https://doi.org/10.1016/j.coastaleng.2019.103593
  5. Bradford, S.F. (2000). Numerical simulation of surf zone dynamics. Journal of Waterway Port Coastal and Ocean Engineering, 126, 1-13. https://doi.org/10.1061/(ASCE)0733-950X(2000)126:1(1)
  6. Chella, M.A., Bihs, H., Myrhaug, D. and Muskulus, M. (2015). Breaking characteristics and geometric properties of spilling breakers over slopes. Coastal Engineering, 95, 4-19. https://doi.org/10.1016/j.coastaleng.2014.09.003
  7. Christensen, E.D. (2006). Large eddy simulation of spilling and plunging breakwaters. Coastal Engineering, 53, 463-485. https://doi.org/10.1016/j.coastaleng.2005.11.001
  8. Deo, M.C and Jagdale, S.S. (2003). Prediction of breaking waves with neural networks. Ocean Engineering, 30, 1163-1178. https://doi.org/10.1016/S0029-8018(02)00086-0
  9. Etemad-Shahidi, A., Shaeri, S. and Jafari, E. (2016). Prediction of wave overtopping at vertical structures. Coastal Engineering, 109, 42-52. https://doi.org/10.1016/j.coastaleng.2015.12.001
  10. Fisher, M.A. and Bolles, R.C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395. https://doi.org/10.1145/358669.358692
  11. Formentin, S.M. and Zanuttigh, B. (2019). A Genetic Programming based formula for wave overtopping by crown walls and bullnoses. Coastal Engineering, 152, 103529. https://doi.org/10.1016/j.coastaleng.2019.103529
  12. Goda, Y. (1970). A synthesis of breaker indices. Transactions of the Japan Society of Civil Engineers, 2(2), 39-40.
  13. Goda, Y. (2010). Reanalysis of regular and random breaking wave statistics. Coastal Engineering Journal, 52(1), 71-106. https://doi.org/10.1142/S0578563410002129
  14. Hieu, P.D., Katsutoshi, T. and Ca, V.T. (2004). Numerical simulation of breaking waves using a two-phase flow model. Applied Mathematical Modelling, 28, 983-1005. https://doi.org/10.1016/j.apm.2004.03.003
  15. Huber, P.J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. https://doi.org/10.1214/aoms/1177703732
  16. Ishida, H. and Yamaguchi, N. (1983). A theory for wave breaking on slopes and its application. Proceedings of 30th Japanese Conference on Coastal Engineering, JSCE, 34-38 (in Japanese).
  17. James, S.C., Zhang, Y. and O'Donncha, F. (2018). A machine learning framework to forecast wave conditions. Coastal Engineering, 137, 1-10. https://doi.org/10.1016/j.coastaleng.2018.03.004
  18. Kakuno, S., Sugita, T. and Goda, T. (1996). Effects of wave breaking on entrainemnt of oxygen, a review. Proceedings of 43rd Japanese Conderence on Coastal Engineering, JSCE, 1211-1215 (in Japanese).
  19. Kazeminezhad, M.H. and Etemad-Shahidi, A. (2015). A new method for the prediction of wave runup on vertical piles. Coastal Engineering, 98, 55-64. https://doi.org/10.1016/j.coastaleng.2015.01.004
  20. 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.
  21. Kim, D.H. and Park, W.S. (2005). Neural network for design and reliability analysis of rubble mound breakwaters. Ocean Engineering, 32, 1332-1349. https://doi.org/10.1016/j.oceaneng.2004.11.008
  22. 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. https://doi.org/10.9765/KSCOE.2011.23.1.109
  23. Lara, J.L., Losada, I.J. and Liu, P.L.F. (2006). Breaking waves over mild gravel slope: Experimental and numerical analysis. Journal of Geophysical Research, 111, 1-26.
  24. Lee, J.S. and Suh, K.D. (2016). Calculation of stability number of tetrapods using weights and biases of ANN model. Journal of Korean Society of Coastal and Ocean Engineers, 28(5), 277-283. https://doi.org/10.9765/KSCOE.2016.28.5.277
  25. 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 https://doi.org/10.9765/KSCOE.2020.32.4.262
  26. 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. https://doi.org/10.1016/j.oceaneng.2005.10.026
  27. Lin, P. and Liu, P.L.F. (1998). A numerical study of breaking waves in the surf zone. Journal of Fluid Mechanics, 359, 239-264. https://doi.org/10.1017/S002211209700846X
  28. Liu, Y., Niu, X. and Yu, X. (2011). A new predictive formula for inception of regular wave breaking. Coastal Engineering 58(9), 877-889. https://doi.org/10.1016/j.coastaleng.2011.05.004
  29. McCowan, J. (1984). On the highest wave of permanent type. Philosophical Magazine, 38(5), 351-358.
  30. Miche, R. (1944). Mouvements ondulatoires de la mer en profondeur constante ou decroissante. Annales de Ponts et Chaussees, 114, 26-78, 270-292, 369-406 (in French).
  31. 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. https://doi.org/10.1111/j.1749-6632.1949.tb27281.x
  32. Oh, N.S. and Jeong, S.T. (2015). The Prediction of Water Temperature at Saemangeum Lake by Neural Network. Journal of Korean Society of Coastal and Ocean Engineers, 27(1), 56-62. https://doi.org/10.9765/KSCOE.2015.27.1.56
  33. Pedregosa, F., Varoquaux, G., Gramfort, A. Michel, V., Thirion, B., Grisel, O., Blondel, M, Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A. and Cournapeau, D. (2011). Scikitlearn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
  34. 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. https://doi.org/10.1142/S0578563406001489
  35. Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-153. https://doi.org/10.1016/j.knosys.2011.07.016
  36. Sakai, S., Kazumi, S., Ono, T., Yamashita, T. and Saeki, H. (1986). Study on wave breaking and its resulting entrainment of air. Proceedings of 33rd Japanese Conference on Coastal Engineering, JSCE, 16-20 (in Japanese).
  37. Samuel, A.L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229. https://doi.org/10.1147/rd.33.0210
  38. Smith, E.R. and Kraus, N.C. (1990). Laboratory study on macrofeatures of wave breaking over bars and artificial reefs. U.S. Army Corps of Engineers Technical Report CREC-90-12, WES, 232p.
  39. Stringari, D.L., Harris, D.L. and Power, H.E. (2019). A novel machine learning algorithm for tracking remotely sensed waves in the surf zone. Coastal Engineering, 147, 149-158. https://doi.org/10.1016/j.coastaleng.2019.02.002
  40. Stokes, G.G. (1880). Appendices and supplement to a paper on the theory of oscillatory waves. Mathematical and Physical Papers, 1, 219-229.
  41. Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York.
  42. 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. https://doi.org/10.1080/21664250.2019.1579459
  43. Yi, J.K., Ryu, K.H., Baek, W.D. and Jeong, W.M. (2017). Wave height and downtime event forecasting in harbour with complex topography using auto-regressive and artificial neural networks Models. Journal of Korean Society of Coastal and Ocean Engineers, 29(4), 180-188. https://doi.org/10.9765/KSCOE.2017.29.4.180
  44. Zhao, Q., Armfield, S. and Tanimoto, K. (2004). Numerical simulation of breaking waves by a multi-scale turbulence model. Coastal Engineering, 51, 53-80. https://doi.org/10.1016/j.coastaleng.2003.12.002