Coastal erosion is caused by various factors, such as harbor development along coastal areas and climate change. Erosion has been accelerated recently due to sea level rises, increased occurrence of swells, and higher-power storm waves. Proper understanding of the complex coastal erosion process is vital to prepare measures when they are needed. Monitoring systems have been widely established around a high portion of the Korean coastline, supported by several levels of governments, but valid analysis of the collected data and the following preparation of measures have not been highly effective yet. In this paper, we use a drone to obtain bed material images, and an analysis system to predict the representative grain size of beach sands from the images based on artificial intelligence (AI) analysis. The predicted grain sizes are verified via field samplings. Field bed material samples for the particle size analysis are collected during two seasons, while a drone takes photo images and the exact positions are simultaneously measured at Jangsa beach, Republic of Korea. The learning and testing results of the AI technology are considered satisfactory. Finally, they are used to diagnose the overall stability of Jangsa beach. A beach diagnostic grade is proposed here, which reflects the topography of a beach and the distribution of sediments on the beach. The developed beach diagnostic grade could be used as an indicator of any beach stability on the east coast of the Republic of Korea. When the diagnostic grade changes rapidly at a beach, it is required to undergo thorough investigation to understand the reason and foresee the future of the beach conditions, if we want the beach to function as well as before.

The edge wave on a uniform-sloped seabed was described by the velocity-potential function by Mok and Yeh in 1999. Edge waves cannot be extended above a certain level from the still-water level, and the upper limit of the run-up of the edge waves for given conditions is found here. In this study, quantitative mass transport by the edge waves of the beach is introduced. The maximum run-up height is decided from the wave’s amplitude at shoreline, and the maximum run-up distance from the shoreline is proportional to the wavelength of the edge waves. The fluid alongshore-mass-transport profile shows that the strongest mass transport rate corresponds to the position offshoreward multiplied by 0.0362 times the wavelength, and its magnitude is 1.23 times the mass-transport rate at the shoreline. The maximum cross-sectional total mass-transport rate is 0.214 times the mass transport at the shoreline, multiplied by the wavelength for the maximum run-up condition. This study suggests that edge waves cannot be increased infinitely and that there is a maximum run-up on the coast.

Increased coastal human intervention and climate change are leading to rapid changes in coastal areas and often causing catastrophic damage to resident populations. The ShorelineS model was introduced, which can explain drastic beach changes were simulated. According to the modeling results, the installation of Gungchon Port caused rapid sedimentation in the south of the port. It was reviewed that the erosion and sedimentation tendency of the beach is changed after the installation breakwater in front of the beach, the erosion in the central part of the beach was somewhat suppressed, and the beach was deposited in the shape of tombolo on the back of the beach. The installation of artificial structures dramatically changes the equilibrium state of the beach and increases the erosion of the direct wave near the structure. The ShorelineS model was the advantage of being able to properly reproduce changes in the coastline by inputting relatively simplified environment and beach information and to quickly predict after installation of artificial structures. The reallity, coastline changes due to various causes, so follow-up studies are needed to predict and effectively respond to this through an advanced model that reflects various conditions.

The west coast of Korea is characterized by a macro-tidal environment, where beach exposure varies significantly with tidal levels, resulting in high spatial variability of beach width and erosion patterns. This study aims to establish an Erosion Control Line (ECL) for Mallipo Beach using long-term beach topographic data collected from 2009 to 2020. For each transect, beach width was statistically estimated for a 30-year return period by calculating the average and standard deviation of surveyed widths and applying the inverse function of the normal cumulative distribution. The variability of shoreline positions was analyzed as an indicator of shoreline sensitivity, allowing the identification of highly vulnerable sections. Based on these analyses, the ECL was derived for three tidal reference levels—Highest Water of Medium Tide (H.W.O.M.T), Highest Water of Neap Tide (H.W.O.N.T), and Mean Sea Level (M.S.L)—according to Korea Hydrographic and Oceanographic Agency (KHOA)’s tidal datums. When the H.W.O.N.T-based beach width was used to define the Target shoreLimit of Erosion Prevention (TLEP), several public facilities were found to fall within the erosion hazard zone. These findings underscore the need for institutionalized coastal setback policies in Korea and highlight the practical value of the proposed ECL method for managing erosion-prone zones.

Due to climate change, waves have become increasingly stronger, making the analysis of beach changes before and after typhoons crucial for addressing beach erosion. This study utilized low-cost, high-efficiency video monitoring to analyze beach changes at 14 locations along Korea's east coast before and after typhoon impacts. Shorelines were extracted from 180 s average orthoimages using the Pixel Intensity Moving Average Extraction technique, and beach areas were calculated. The study focused on the recovery period following typhoon-induced erosion. During Typhoon Goni (2015), erosion reached up to 38% at Bongpo Beach, with a maximum affected area of 7,741 m2 at Goraebul Beach. Post-typhoon recovery exceeded 89%, with most beaches returning to pre-typhoon conditions. The erosion period averaged 7 d, while recovery took approximately 27 d. Erosion was significantly influenced by natural forces such as waves, tides, and wind. The erosion period showed minimal correlation with wave energy, whereas the recovery period exhibited some correlation. Further long-term analysis, incorporating additional wave data and typhoon impact periods, is needed. Future research will aim to collect extensive typhoon data to systematically analyze erosion and recovery cycles in relation to external forces.

Kim, H.; Yoo, H.J., and Lee, J.L., 2021. Nonlinear Kernel Convolutional Neural Network to find median sand particle size. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 1-5. Coconut Creek (Florida), ISSN 0749-0208. Convolutional Neural Network (CNN) has successfully been used in various areas. We focus on predicting various sand particle sizes by applying convolutional neural network with a nonlinear kernel. The nonlinear kernel involves a bias and negative square of subtraction between input image pixel numbers and the kernel coefficients and summation. The convolution layer conform new feature map in Convolutional Neural Network. While using batch gradient descent method to train relevant coefficients and biases, the gradient of the square of subtraction term appears in the whole gradient over each kernel coefficient. The network was examined on regular-sized sands, i.e. 2000, 1000, 500, 250, 125 and 63 micrometer. The network was trained by using various images for each size. It was validated against new images and the absolute error was less than 30 micrometer, respectively, which is satisfactory. The network was applied by using 3 images of sands with size distribution. The results show good validation and satisfactory predictions. In the course of study, several numbers of kernels, kernel sizes, pooling sizes were tried and the optimum architecture for this work was chosen. It is expected that the present network will reduce time and effort in obtaining median sand size in many field projects. The size distribution of sand particles could also be obtained with the present network in the near future.