Acknowledgement
이 논문은 2021~2022년도 창원대학교 자율연구과제 연구비 지원으로 수행된 연구결과이며, 이에 감사드립니다.
References
- Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A. and Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
- Beuzen, T., Goldstein, E.B. and Splinter, K.D. (2019). Ensemble models from machine learning: an example of wave runup and coastal dune erosion. Natural Hazards and Earth System Sciences, 19(10), 2295-2309. https://doi.org/10.5194/nhess-19-2295-2019
- da Silva, S. (2018). Data-driven model identification of guided wave propagation in composite structures. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(11), 1-10. https://doi.org/10.1007/s40430-017-0921-7
- den Bieman, J.P., van Gent, M.R. and van den Boogaard, H.F. (2021). Wave overtopping predictions using an advanced machine learning technique. Coastal Engineering, 166, 103830.
- Formentin, S.M. and Zanuttigh, B. (2019). A genetic programming based formula for wave overtopping by crown walls and bullnoses. Coastal Engineering, 152, 103529.
- Goda, Y. (2010). Reanalysis of regular and random breaking wave statistics. Coastal Engineering Journal, 52(1), 71-106. https://doi.org/10.1142/S0578563410002129
- 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
- Jordan, M.I. and Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
- Kamath, A., Vargas-Hernandez, R.A., Krems, R.V., Carrington Jr, T. and Manzhos, S. (2018). Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy. The Journal of Chemical Physics, 148(24), 241702.
- Kim, H.G., Park, E., Jeong, J., Han, W.S. and Kim, K.-Y. (2016). Groundwater level trend analysis for long-term prediction based on Gaussian process regression. Journal of Soil and Groundwater Environment, 21(4), 30-41 (in Korean). https://doi.org/10.7857/JSGE.2016.21.4.030
- Kim, J., Kim, T., Oh, S.-H., Do, K., Ryu, J.-G. and Kim, J. (2021). Deep visual domain adaptation and semi-supervised segmentation for understanding wave elevation using wave flume video images. Scientific Reports, 11, 21776.
- Knudde, N., Raes, W., De Bruycker, J., Dhaene, T. and Stevens, N. (2020). Data-efficient gaussian process regression for accurate visible light positioning. IEEE Communications Letters, 24(8), 1705-1709. https://doi.org/10.1109/lcomm.2020.2990950
- 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 (in Korean). https://doi.org/10.9765/KSCOE.2016.28.5.277
- 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). https://doi.org/10.9765/KSCOE.2020.32.4.262
- Oh, S.-H. and Lee, D.S. (2018). Two-dimensional wave flume with water circulating system for controlling water level. Journal of Korean Society of Coastal and Ocean Engineers, 30(6), 337-342 (in Korean). https://doi.org/10.9765/KSCOE.2018.30.6.337
- Rasmussen, C.E and Williams, C.K. (2006). Gaussian processes for machine learning, MIT press, Cambridge, Massachusetts.
- Stringari, C.E., Guimaraes, P.V., Filipot, J.F., Leckler, F. and Duarte, R. (2021). Deep neural networks for active wave breaking classification. Scientific Reports, 11(1), 1-12. https://doi.org/10.1038/s41598-020-79139-8