지도학습을 이용한 새로운 선형 쇄파지표식 개발
A Proposal of New Breaker Index Formula Using Supervised Machine Learning
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초록
연안에서 천수변형에 의해 발생하는 쇄파는 표사이동, 연안류의 생성, 충격파압의 발생 등과 같은 연안역의다양한 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 많은경험식이 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 연구분야에있어서 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 경험공식에 비해 유사한 예측성능을 보였다.
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.
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