가우시안 과정 회귀 및 인공신경망에 의한 불규칙파 조파기 입력파고 추정
Estimation of the Input Height of Irregular Waves Generated at a Wave Maker by Using Gaussian Process Regression and Artificial Neural Networks
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참고문헌
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초록
2차원 조파수조 내에서 취득된 불규칙파 실험 데이터를 이용하여 조파기 입력파고를 예측하는 인공신경망(NN) 및 가우시안 과정 회귀(GPR) 모델을 수립하고 그 성능을 평가 및 비교하였다. 두 모델 모두 불규칙파 조파 시목표파고를 재현할 수 있는 입력파고를 매우 높은 정확도로 추정할 수 있었으며, NN 모델에 비해서는 GPR 모델의성능이 더 우수한 것으로 나타났다. 본 연구 결과를 바탕으로 조파수조에서 수리실험을 수행할 때 조파기 입력파고설정 시 시행착오를 줄임으로써 실험에 소요되는 시간을 줄일 수 있을 것으로 기대된다.
Abstract
A neural network (NN) and Gaussian process regression (GPR) model for predicting the input wave height of the wave generator were established, and their performance was evaluated and compared using irregular wave data acquired in a two-dimensional wave flume. Both models were able to predict the input wave height that can produce the target wave height of irregular waves with a very high accuracy. Among the two models, the GPR model showed better performance than the NN model. Based on the results of this study, it is expected to reduce the time required for experiments by shortening trial and error in setting the input wave height of the wave generator when conducting physical experiments in a wave flume.
참고문헌 (16)
- Abouhalima, M., "Machine learning in coastal engineering : applications, challenges, and perspectives", Journal of Marine Science and Engineering, 12(4), pp.638~ (2024)
- Cheng, H., "Prediction of irregular wave(current)-induced pore water pressure around monopile using machine learning methods", Coastal Engineering, 182, pp.104291~ (2023)
- Giaremis, S., "Storm surge modeling in the AI era : Using LSTM-based machine learning for enhancing forecasting accuracy", Coastal Engineering, 191, pp.104532~ (2024)
- Goldstein, E. B., "A review of machine learning applications to coastal sediment transport and morphodynamics", Earth-science Reviews, 194, pp.97~108 (2019)
- Jordan, M. I., "Machine learning : Trends, perspectives, and prospects", Science, 349(6245), pp.255~260 (2015)
- Kim, T., "Review on applications of machine learning in coastal and ocean engineering", Journal of Ocean Engineering and Technology, 36(3), pp.194~210 (2022)
- Knudde, N., "Data-efficient gaussian process regression for accurate visible light positioning", IEEE Communications Letters, 24(8), pp.1705~1709 (2020)
- Liu, Z., "Machine learning motivated data imputation of storm data used in coastal hazard assessments", Coastal Engineering, 190, pp.104505~ (2024)
- Lucero, F., "Improving WAVEWATCH III hindcasts with machine learning", Coastal Engineering, 185, pp.104381~ (2023)
- Oh, J. -E., "Estimation of the input wave height of the wave generator for regular waves by using artificial neural networks and Gaussian process regression", Journal of Korean Society of Coastal and Ocean Engineers, 34(6), pp.315~324 (2022)
- Oh, S. -H., "Two-dimensional wave flume with water circulating system for controlling water level", Journal of Korean Society of Coastal and Ocean Engineers, 30(6), pp.337~342 (2018)
- Parker, K., "Emulation as an approach for rapid estuarine modeling", Coastal Engineering, 150, pp.79~93 (2019)
- Pourzangbar, A., "Machine learning application in modelling marine and coastal phenomena : a critical review", Frontiers in Environmental Engineering, 2, pp.1235557~ (2023)
- Rasmussen, C. E., "Gaussian processes for machine learning" (2006), MIT press
- Shashank, V. G., "A new surge index with the incorporation of cyclone track approach angle information for the Bay of Bengal", Coastal Engineering, 188, pp.104429~ (2024)
- Wang, K., "Exact Gaussian processes on a million data points", Advances in Neural Information Processing Systems, 32 (2019)