## 1 Introduction  
As shipbuilding technology continues to improve, ships are getting bigger and faster, and the volume of seaborne trade worldwide increases every year [1]. South Korea, surrounded by the sea on three sides, has developed fisheries and marine industries, while 99.7% of imports and exports are transported by ship [2]. It is also strategically located in the center of Northeast Asia, serving as an important logistics hub. Due to geographical influences, however, port areas and coastal waters used by ships for safe passage are limited. As the size of ships gets larger and the volume of ships passing through limited waterways increases, the risk of marine accidents increases. In South Korea, the number of marine accidents has increased by 1080.8%, from 335 in 1981 to 3156 in 2020, and the social costs are enormous [3]. The Korean coast guard responds to marine accidents by using surveillance means such as coast guard ships, aircraft, and Vessel Traffic Service (VTS). However, monitoring per ship is so broad that it is more focused on follow-up than on preventing accidents [4]. The VTS can help to improve maritime safety by providing safety information to ships, but this also has some limitations. It is imperative to predict the risk of marine accidents in advance to prevent or reduce marine accidents by means limited to such a wide range of areas. If the risk of marine accidents can be predicted in advance, then limited equipment and human resources can be deployed effectively. Thus, forecasting the risk of marine accidents is crucial for preventing or reducing marine accidents using limited surveillance capabilities. Based on marine accident statistics data, previous studies used the Markov model [5] or ANOVA analysis [6] to classify the type of marine accidents, and [PERSON] et al. [7] identified factors affecting accidents of ships controlled by VTS using a regression model. [PERSON] et al. [8] evaluated the safety of maritime transport utilizing Markov chains, [PERSON] [9] examined the benefits and disadvantages of using Bayesian networks for predicting marine accidents, and [PERSON] [10] analyzed seafarers' behavioral errors using Hidden Markov models. As such, a variety of statistical and probability-based methods have been investigated to prevent marine accidents. Recently, research on deep learning has been conducted in many fields with an understanding of artificial intelligence (AI). [PERSON] et al. [11] developed a regression model, an artificial neural network (ANN), and a structural equation model (SEM) to predict the frequency of traffic accidents, respectively. [PERSON] et al. [12] constructed a traffic accident prediction model using deep learning technology and proposed that a deep learning approach is helpful for traffic accident-related research by comparing the results with traditional analysis methods. [PERSON] et al. [13] analyzed that traffic flow is the most critical factor in traffic accidents using the deep learning model. [PERSON] et al. [14] constructed crash modeling using Deep Brief Networks (DBN). They demonstrated that deep learning techniques are excellent as an alternative to predicting the frequency of traffic accidents using real-world crash datasets. In addition, [PERSON] [15] developed a long-short term memory (LSTM) neural network to predict and visualize traffic accidents in Switzerland, and [PERSON] and [PERSON] [16] analyzed that the recurrent neural network (RNN) method is advantageous in predicting the severity of traffic accidents compared to conventional NNs using traffic accident records in Malaysia for six years. [PERSON] and [PERSON] [17] proposed a LSTM model to forecast a traffic accident occurrence pattern. Many studies have used deep learning to predict traffic accidents and demonstrate that the RNN model is optimized for predicting time series data compared to other algorithms.