In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

Among the fishing vessels operating in the coastal waters, jigging fishing vessels were considered representative vessels engaged only by wind, sea, tide, and external force. Then, a fishing vessel with a length of shorter than 10 m from July 1, 2018 to August 5, 2019 was studied to obtain a drift prediction model by multiple regression analysis. In the correlation analysis between variables for leeway of speed and direction, the speed and direction of tidal seem to be the most affected in coastal waters. Therefore, it should be considered an explanatory variable when conducting drift tests. As a result of multiple regression analysis on the predicted equations of leeway speed and direction due to the external force on the drift of the fishing vessel, p < 0.000 was considered significant in the F-test, but the coefficient of determination was 55.2% and 37.8%. The effect on the predicted leeway speed was in the order of the tidal speed and current speed. In addition, the impact on the predicted leeway direction was in the order of the tidal speed and current speed.

To decrease area of the repository for high-level radioactive waste, enhancing the disposal efficiency is needed for public acceptance. Previous studies regarding the performance assessment of KRS and KRS+ repository did not consider area-based variations of the geothermal gradient and rock thermal properties in Korea. This research estimated deposition hole spacing based on performance assessment of a repository using the distribution of geothermal gradient and rock thermal properties in Korea to increase disposal efficiency. Distributions of geothermal gradient, rock thermal properties were investigated based on 2019 Korea geothermal atlas published by Korea Institute of Geoscience and Mineral Resources (KIGAM). Effect of thermal performance parameters was analyzed using coupled thermal-hydraulic numerical simulations, and effect of rock thermal conductivity and deposition hole spacing on the maximum temperature of buffer was relatively large. In addition, distribution maps of thermal performance of a repository and deposition hole spacing were plotted using thermal performance parameters-maximum temperature of buffer regression equations and GIS data given by KIGAM. In the regions showing the highest maximum temperature of buffer in Korea, required deposition hole spacings were 10.5 m, 10.0 m, 10.1 m, respectively for KJ-II, MX-80, and FEBEX bentonite cases, and thereby additional disposal area of 40%, 33.3%, and 34.7% were required compared to that of the KRS+ repository. On the other hand, high disposal efficiency can be obtained in the regions showing the low maximum temperature of bentonite buffer. The methodology provided in this research can be used as one of the references for the selection of domestic candidate repository sites. Additional mechanical performance analysis should be conducted using distributions of mechanical properties of rock mass in Korea.

The fishery compensation by marine spatial planning such as routeing of ships and offshore wind farms is required objective data on whether fishing vessels are engaged in a target area. There has still been no research that calculated the number of fishing operation days scientifically. This study proposes a novel method for calculating the number of fishing operation days using the fishing trajectory data when investigating fishery compensation in marine spatial planning areas. It was calculated by multiplying the average reporting interval of trajectory data, the number of collected data, the status weighting factor, and the weighting factor for fishery compensation according to the location of each fishing vessel. In particular, the number of fishing operation days for the compensation of driftnet fishery was considered the daily average number of large vessels from the port and the fishery loss hours for avoiding collisions with them. The target area for applying the proposed method is the routeing area of ships of Jeju outer port. The yearly average fishing operation days were calculated from three years of data from 2017 to 2019. As a result of the study, the yearly average fishing operation days for the compensation of each fishing village fraternity varied from 0.0 to 39.0 days. The proposed method can be used for fishery compensation as an objective indicator in various marine spatial planning areas.

Due to the high risk of personal injury and property damage, the safety of maritime transport is an important concern for everyone involved. Ship navigation officers usually look up a ship's collision profile for safety-at-sea information before entering an unknown coastal area. Near-ship collisions are very important when assessing the potential risk of shipping. This paper undertakes a ship encounter risk assessment, involving analyses of the trajectory data of merchant ships and then extracts ship encounter data, creates a probabilistic model to determine whether an encounter event is a near miss, and suggests risk indicators. The proposed method will be useful for navigators to plan safe passages.

In the area around Jeju Island, the squid jigging fishery and the hair-tail angling are popular. Therefore, the study on the characteristics of the formation and shift of fishing grounds is very important. We have received and analyzed AIS data of all vessels around Jeju Island from October 16, 2016 to October 16, 2017, and extracted the positions of the fishing vessels with the same operational characteristics as the fishing vessels of their fisheries. The distribution chart of the frequency of fishing vessels appearing in each predefined fishing grid (1 NM × 1 NM) was analyzed. So we took a analogy with the monthly shift of fishing grounds. Many fishing vessels appeared in the seas around Jeju Island from November 2016 to January 2017, and the frequency of their appearance was maintained. In November, however, fishing vessels were mostly concentrated in coastal waters. Yet, the density gradually weakened as they moved into January. From February, the frequency itself began to decline, making it the worst in April. The high concentration of fishing vessels in the waters leading from Jeju Island's northwest coast to south coast in November is believed to be related to the yellowtail fishery that are formed annually in the coastal waters off the island of Marado. In May 2017, the appearance frequency of fishing vessels increased and began to show a concentration in coastal waters around Jeju Island. Fishing vessels began to flock in waters northwest of Jeju Island beginning in July and peaked in August, and by September, fishing vessels were moving south along the coast of Jeju Island, weakening the density and spreading out. Between July and August, fishing vessels were concentrated in waters surrounding Jeju Island, which is believed to be related to the operations of fishing vessels for the squid jigging fishery and the hair-tail angling.

We have studied the efficient operation of the radar and the appropriateness of the installation location, when constructing the VTS system. As the Civil-Military Complex Harbour (Kangjeong Port) is completed in 2016, we set the control area within 10 nautical miles centering on Kangjeong Port, and found out and removed the operational radar blind area of VTS system to provide safe navigation information for vessels that navigating this area. Assuming that two international cruise ships entering at the same time, we performed the radar simulation and compared the images by considering the three sites of Kangjeong Port, Miaksan and Seoguipo Port. Simulation results for a single radar installed at Kangjung Port indicate that the blind area was largely affected by two large cruise ships and the surrounding islands. The blind area due to Kogunsan was considerably large when installed in Miaksan, but the blind area due to the influences of Beomseom, Moonseom and Seopseom was negligibly large. It seems that additional radar installation is necessary as a complementary solution to solve this blind area. When two radars were installed at Miaksan and Kangjeong Port, the residual blind area due to the Seopseom was 0.25 km2 at 0.1~0.33 nautical miles in the southeast direction from Seopseom. In addition, the remaining blind area with two cruise ships mutually influenced was 0.18 km2, which did not occur with a single cruise ship.

In the era of e-Navigation, it is important to deliver maritime traffic information from a shore based station to all navigating vessels. However, in a vessel boarding system, there is a limit to the amount of raw traffic data that can be processed. In this paper, we used the Automatic Identification System (AIS) data as metadata to build up the maritime traffic gridded database by projecting traffic data on a geographic coordinate system. In order to apply this database to the image layer for transferring to the ship efficiently, we have developed a maritime traffic display layer and route traffic information layer. All simulated data was collected and analyzed with the AIS in a Vessel Traffic Service(VTS) center.