Abstract
Beach profiles are constantly changing due to external ocean forces. Estimating these changes is crucial to understanding and addressing coastal erosion issues, such as shoreline advance and retreat. To estimate beach profile changes, obtaining long-term, high-resolution spatiotemporal beach profile data is essential. However, due to the limited availability of beach profile survey data both on land and underwater along the coast, generating continuous, high-resolution spatiotemporal beach profile data over extended periods is a critical technological challenge. Therefore, we herein developed a long short-term memory-based encoder–decoder network for effective spatiotemporal representation learning to estimate beach profile responses on temporal scales from weeks to months from coastal hydrodynamics. The proposed approach was applied to 12 transects from seven beaches located in three different littoral systems on the east coast of the Korean Peninsula, where coastal erosion problems are severe. The performance of the proposed method demonstrated improved results compared with a recent study that performed the same beach profile estimation task, with an average root mean square error of 0.50 m. Moreover, most of the results exhibited a reasonably accurate morphological shape of the estimated beach profile. However, instances where the results exceed the average error are attributed to extreme beach morphological changes caused by storm waves such as typhoons.