Hybrid Reconstruction of Sea Level at Dokdo in the East Sea Using Machine Learning and Geospatial Interpolation (1993–2023)

Sea level variability in the East Sea (Sea of Japan) and the Northwest Pacific poses challenges for coastal risk management due to the scarcity of long-term observations at remote locations such as Dokdo (Dok Island). This study reconstructs a continuous monthly sea level record at Dokdo from 1993 to 2023 by imputing gaps in 13 nearby Permanent Service for Mean Sea Level tide gauge stations using eight machine learning models and geospatial interpolation methods. The ensemble mean of Machine Learning-based imputations produced physically realistic and temporally coherent timeseries, preserving both seasonal and interannual variability. Sea level at Dokdo, estimated via inverse distance weighting, aligned well with satellite altimetry from Copernicus Marine Service and exhibited strong regional coherence with nearby stations. These results demonstrate that a hybrid framework combining statistical imputation, Machine Learning, and inverse barometric correction can effectively reconstruct sea level in data-sparse marine regions. The methodology provides a scalable tool for monitoring long-term trends and validating satellite and model products in marginal seas like the East Sea.