| Title |
Prediction of Beach Profile Change Using Machine Learning Technique |
| Authors |
심규태(Shim, Kyu Tae) ; 조병선(Cho, Byung Sun) ; 김현동(Kim, Hyun Dong) ; 김규한(Kim, Kyu Han) |
| DOI |
https://doi.org/10.12652/Ksce.2022.42.5.0639 |
| Keywords |
머신러닝; 양빈; 수리모형실험; 해빈단면변형; 바람 |
| 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 needsto 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 modelexperiments, field monitoring and etc. So we attempt to reproduce the tendency of beach erosion and deposition and predict possiblephenomena in the future using machine learning techniques for phenomena that it is not able to be interpreted by numerical models. weused the hydraulic experiment results for the training data, and the accuracy of the prediction results according to the change in the trainingmethod was simultaneously analyzed. As a result of the study it was found that topographic changes using machine learning tended tobe similar to those of previous studies in short-term predictions, but we also found differences in the formation of scour and sandbars. |