KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Prediction of Beach Profile Change Using Machine Learning Technique

머신러닝을 이용한 해빈단면 변화 예측

  • 심규태 (가톨릭관동대학교 토목공학과) ;
  • 조병선 (첨단해양공간개발연구센터) ;
  • 김규한 (가톨릭관동대학교 토목공학과)
  • Received : 2021.11.24
  • Accepted : 2022.05.26
  • Published : 2022.10.01
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Abstract

In areas where large-scale sediment transport occurs, it is important to apply appropriate countermeasure method because the phenomenon tends to accelerate by time duration. Among the various countermeasure methods applied so far, beach nourishment needs to be reviewed as an erosion prevention measure because the erosion pattern is mitigated and environmentally friendly depending on the particle size. In the case of beach nourishment. a detailed review is required to determine the size, range, etc., of an appropriate particle diameter. In this study, we investigated the characteristics of the related topographic change using the change in the particle size of nourishment materials, the application of partial area, and the condition under the coexistence of waves and wind as variables because those factors are hard to be analyzed and interpreted within results and limitation of that the existing numerical models are not able to calculate and result out so that it is required that phenomenon or efforts are reviewed at the same time through physical model experiments, field monitoring and etc. So we attempt to reproduce the tendency of beach erosion and deposition and predict possible phenomena in the future using machine learning techniques for phenomena that it is not able to be interpreted by numerical models. we used the hydraulic experiment results for the training data, and the accuracy of the prediction results according to the change in the training method was simultaneously analyzed. As a result of the study it was found that topographic changes using machine learning tended to be similar to those of previous studies in short-term predictions, but we also found differences in the formation of scour and sandbars.

대규모 표사이동으로 인해 침·퇴적이 발생되는 해안에서는 시간이력에 따라 그 현상이 가속화되는 경향이 있기 때문에 적절하고도 시급한 대책을 강구하는 것이 중요하다. 해안침식의 대책방안 중 환경친화적 대책으로 알려진 양빈공법의 경우 입경의 크기에 따라 침식양상이 변화되므로 적정 입경의 크기, 범위 등에 대해 결정하기 위해서는 면밀한 검토가 필요하다. 본 연구에서는 양빈사의 입경변화와 부분양빈의 적용, 파랑과 바람이 공존하는 조건 등을 변수로 설정하였을 때 발생되는 지형변화의 특성을 검토하고자 하였다. 이러한 요인들은 수치모형실험에서 해석하기 어려운 부분이 존재하기 때문에 수리모형실험을 통해 정성적인 해석을 수행하거나 양빈수행 이후에 현장모니터링 등을 통해 그 효과를 검토하게 된다. 하지만 실험과 모니터링 등은 제반사항이 발생되기 때문에 다양한 조건에 대한 예측 연구에는 어려움이 존재한다. 본 논문에서는 빅데이터의 활용을 통한 머신러닝 기법을 이용하여 침·퇴적 경향을 재현함으로써 발생 가능한 현상에 대해 예측함과 동시에 머신러닝 기법의 적용성을 검토하고자 하였다. 학습데이터는 수리모형실험결과를 이용하였으며 연구결과 머신러닝을 이용한 지형변화는 단기예측의 경우 기존연구와 유사한 경향을 보이는 것으로 나타났으나 세굴 및 모래톱의 형성 등에서는 다소 차이가 존재하는 것을 확인할 수 있었다.

Keywords

Acknowledgement

본 논문은 2021 CONVENTION 논문을 수정·보완하여 작성되었습니다.

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