Work

Comparative Analysis and Validation of LSTM and GRU Models for Predicting Annual Mean Sea Level in the East Sea: A Case Study of Ulleungdo Island

Year: 2025

Type: article

Abstract: This study presents a deep learning-based model for predicting annual mean sea level (MSL) in the East Sea, with a focus on the Ulleungdo Island region, which maintains an independent vertical datum. To account for long-term tidal variability, the model enables continuous estimation of hourly and annual MSL values. Two recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were constructed and compared. Observational tide gauge data from 1 January 2000 to 3 August 2018 (covering 18.6 years and a full tidal nodal cycle) were preprocessed through missing-value and outlier treatment, followed by min–max normalization, and then structured for sequential learning. Comparative analysis demonstrated that the GRU model slightly outperformed the LSTM model in predictive accuracy and training stability. As a result, the GRU model was selected to produce annual MSL forecasts for the period 2018–2021. The GRU achieved a mean RMSE of approximately 0.44 cm during this prediction period, indicating robust performance in forecasting hourly sea level variations. The findings highlight the potential of deep learning methods to support vertical datum determination in island regions and to provide reliable sea level estimates for integration into coastal and oceanographic modeling. The proposed approach offers a scalable framework for long-term sea level prediction under evolving geodetic conditions.

Source: Applied Sciences

DOI: 10.3390/app152011067

Authors: Tae-Yun Kim, Hong-Sik Yun, Hyung-Mi Yoon, Seung-Jun Lee

Institution: Sungkyunkwan University

Language: English

Topic: Coastal and Marine Dynamics

Subfield: Earth-Surface Processes

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896