## 4 Discussion  
Many researchers studied the prediction of the number of marine accidents, but no research was conducted on the prediction of marine accidents according to the navigator's duty time. In addition, existing studies related to predicting the number of marine accidents have been conducted via different methods. However, studies related to deep learning, such as LSTM, have been relatively insufficient. To the best of our knowledge, this was the first attempted study to predict the number of marine accidents using LSTM. In this study, marine accidents that occurred in Korea over the past 39 years from 1981 to 2019 were classified by navigator's duty time, and the number of marine accidents by duty time in 2020 was predicted through the LSTM model. The prediction results were compared with the actual number of marine accidents in 2020. This study showed that the number of marine accidents by the navigator's watch duty time can be predicted using LSTM through time series data. As a result of comparing the number of marine accidents predicted using the LSTM model with the actual number of marine accidents by duty time in 2020, the proposed LSTM models performed reliably. The MAPEs for all four LSTM models were less than 0.1, which means that the accuracies of the proposed LSTMs were greater than 90%. The best LSTM model was LSTM3, with the smallest MAPE, as shown in Table 7. We found that two LSTM layers (i.e., LSTM3 (MAPE = 0.059) and LSTM4 (MAPE = 0.065)) performed better than a single LSTM layer (i.e., LSTM1 (MAPE = 0.066) and LSTM2 (MAPE = 0.076)). In terms of the effect of hidden units, we noticed that 64 hidden units were better than 128 or 256 hidden units. In addition, the best batch size for each model varied from 18, 24, 30, and 6, respectively. These results can help us guide in the building of a more complex LSTM network structure for our future studies. To explore the advantage of the LSTM models, we also implemented an ARIMA model, which is a traditional statistical algorithm for time series forecasts. We also compared the performance results of the LSTMs with the ARIMA model. It was shown that all LSTMs outperformed the ARIMA model (MAPE = 0.089). This result proved the superiority of LSTM compared to the ARIMA. Thus, we can say that the LSTM is better than the ARIMA at predicting the frequency of marine accidents by watch duty hours. Nevertheless, this study still has several limitations. First, although we tuned several hyperparameters, there are more hyperparameters that could significantly affect the performance of the LSTM models. Also, the application of various deep learning algorithms in marine accident prediction models other than LSTM is required. However, since this study is the first attempt to make marine accident predictions using deep learning technology, it can be a guideline when making a complex model for forecasting marine accidents in the future. In addition, this study only used data on the frequency of marine accidents, as this is a univariate time series forecasting study which only uses the previous values of the time series to predict its future values. No other factors were included in this study. Regardless, it is necessary to expand the prediction scope of marine accidents by using time series data with different variables such as weather, human errors, traffic density, and mechanical defects that may be involved in marine accidents. Since this study is a leading study in predicting accidents using deep learning, we believe that it can be a stepping-stone