Kim, D.-Y.; Park, S.-H.; Woo, S.-B.; Jeong, K.-Y., and Lee, E.-I., 2017. Sea level rise and storm surge around the southeastern coast of Korea. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 239–243. Coconut Creek (Florida), ISSN 0749-0208. The sea level rise around the Korean peninsula has become a significant factor that can have a catastrophic impact on low-lying coastal regions. The risk of storm surges can be intensified by rising sea levels, which will create more damaging flood conditions in coastal areas. This study focused on the long-term variations of the mean sea level (MSL) and the annual maximum surge heights (AMSH), which were influenced by global warming and strengthening typhoons. The trends of MSL and AMSH were estimated using the sea level data at eight stations along the southern and eastern coast of Korea over the past 40 years (~2014). The trend was ...

In this study, we estimated drifter tracking over seawater using machine learning and evolutionary search techniques. The parameters used for the prediction are the hourly position of the drifter, the wind velocity, and the flow velocity of each drifter position. Our prediction model was constructed through cross-validation. Trajectories were affected by wind velocity and flow velocity from the starting points of drifters. Mean absolute error (MAE) and normalized cumulative Lagrangian separation (NCLS) were used to evaluate various prediction models. Radial basis function network showed the lowest MAE of 0.0556, an improvement of 35.20% over the numerical model MOHID. Long short-term memory showed the highest NCLS of 0.8762, an improvement of 6.24% over MOHID.

Lim, C.; Park, S.-H.; Kim, D.-Y.; Woo, S.-B., and Jeong, K.-Y., 2017. Influence of steric effect on the rapid sea level rise at Jeju Island, Korea. In: Lee, J.L.; Griffiths, T.; Lotan, A.; Suh, K.-S., and Lee, J. (eds.), The 2nd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 79, pp. 189–193. Coconut Creek (Florida), ISSN 0749-0208. According to a report by the Korea Hydrographic and Oceanographic Agency (KHOA), the sea level rise in west coast (1.31 mm/yr), east coast (2.69 mm/yr) and south coast (2.05 mm/yr) of Korea are different. Especially, the seas around Jeju Island show one of the most rapid increase of sea level rise in recent years. This investigation on the data from Jeju and Seogwipo tidal station indicated that the sea level rises around Jeju and near Seogwipo were 5.63 mm/yr and 3.75 mm/yr, respectively. This shows that the northern and southern parts of the island have different temporal rates of sea level rise. The purpose of this research is t...

본 연구에서는 바다골재 채취로 인한 해저지형 변화를 파악하기 위해 골재채취가 진행되었던 남·서해 EEZ 내 각각 4개 광구와 7개 광구의 수심도를 2 m 격자로 통합·제작하여 분석하였다. 2008년부터 채취를 시작한 남해 EEZ에서는 전체적으로 평균 5 m 이상으로 채취되었고 최대 채취깊이는 광구 4G에서 19.5 m인 반면 2010년부터 채취한 서해 EEZ에서는 국지적으로 5 m 이상의 집중 채취가 이루어졌고 최대 깊이는 광구 9에서 19.0 m인 것으로 분석되었다. 남·서해 EEZ 골재채취단지의 웅덩이 형태의 해저지형은 채취 종료 6년이 지난 후에도 회복되지 않는 것으로 파악되었다. 연안환경과 달리 수심이 깊고 퇴적물의 공급이 활발하지 않는 EEZ 해역의 특성을 고려할 때 수십 년이 경과하더라도 지형의 완전한 회복은 이루어지지 않을 것으로 판단된다.

Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model.

In maritime search and rescue (SAR), rapid and accurate prediction of a castaway’s drift trajectory is essential because survival probability decreases sharply over time. However, conventional physics-based drift prediction models often exhibit substantial uncertainty because they cannot fully capture complex environmental drivers and the diverse characteristics of drifting objects. To address this limitation, this study proposes a machine learning-based drift prediction framework that integrates observed surface drifter data, OpenDrift simulations, and environmental forcings (wind velocity and current velocity). Within this framework, physics-based drift models and historical observations are treated as structured knowledge that guides the learning process. Five prediction models (linear regression, support vector regression, multi-layer perceptron, light gradient boosting model, and long short-term memory) are employed for direct trajectory prediction and correction of OpenDrift forecasts. The framework further incorporates time-series feature expansion, feature selection using SHapley Additive exPlanations, and incremental learning to adapt to previously unseen drifting objects. In addition, an ensemble strategy is introduced that combines genetic algorithm-based weight optimization with dynamic updates via the weighted majority algorithm, enabling robust performance under real-world maritime uncertainty. Experiments using 2021–2023 surface drifter observations show that the proposed framework reduces mean absolute error by more than 70% and improves normalized cumulative Lagrangian separation by more than 80% compared with OpenDrift. These results demonstrate that the proposed framework not only enables reliable drift prediction but also serves as a practical, knowledge-driven decision support system for maritime SAR operations, supporting more efficient search and rescue planning.