## 5 Deep Learning Algorithm Model Employment
### Model Verification  
Table 4 lists the hyperparameters and network structure of the ultimate DNN model. The MAE and RMSE values were designed from the validation data and test data to determine the efficiency of the ultimate DNN model for new data and address the problem of model overfitting. Table 5 presents the simulation outcomes, where the MAE value of the verification data was 1.280 and the RMSE value was 1.195, while the MAE value of the test data was 0.771 and the RMSE value was 1.310. Therefore, since there is not much difference between the calculated result values of the verification data and the calculated result values of the test data, the validity of the final model for new data is confirmed, and the overfitting of the model is assessed as insignificant. Further, to verify the model, a multiple regression analysis model was constructed that applied the same data as multiple regression analysis (MRA). Multiple regression analysis is largely a statistical process that evaluates and formulates the correlation between independent variables and dependent variables, and which is widely used in predictive domains in various industries and research fields [60]. DNN algorithms can also learn complex nonlinear relationships, but they often face the risk of overfitting. On the other hand, although multiple regression analysis is a relatively simple linear model, it is resistant to overfitting and has excellent generalizability. Therefore, the generalization performance of the DNN can be evaluated by using the multiple regression analysis model to check the performance differences with the DNN model. The MAE and RMSE values of the MRA model were considered in the same way as they were in the DNN model. Table 5 compares the calculation outcomes of the DNN model and the MRA model. As a result of the comparison, the DNN model was found \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Structure** & \multicolumn{2}{c}{**Dropout (0)**} & \multicolumn{2}{c}{**Dropout (0.2)**} \\ \cline{2-5}
**Scenario** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline
5–5–5 & 1.744 & 2.288 & 1.789 & 2.330 \\
10–10–10 & 1.659 & 2.187 & 1.769 & 2.307 \\
25–25–25 & 1.341 & 1.805 & 1.694 & 2.229 \\
50–50–50 & 0.915 & 1.214 & 1.555 & 2.059 \\
75–75–75 & 0.866 & 1.209 & 1.511 & 1.983 \\
100–100–100 & 0.802 & 1.106 & 1.549 & 2.051 \\
200–200–200 & 1.013 & 1.033 & 1.586 & 2.090 \\
300–300–300 & 0.483 & 0.978 & 1.242 & 1.693 \\
400–400–400 & 0.452 & 0.757 & 1.112 & 1.496 \\
500–500–500 & 0.410 & 0.581 & 1.086 & 1.456 \\
600–600–600 & 0.466 & 0.642 & 1.106 & 1.510 \\
700–700–700 & 0.470 & 0.622 & 1.232 & 1.687 \\
800–800–800 & 0.481 & 0.665 & 1.123 & 1.533 \\
900–900–900 & 0.493 & 0.567 & 1.170 & 1.601 \\
1000–1000–1000 & 0.561 & 0.491 & 1.124 & 1.533 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Learning results. Figure 7: Network architecture performance. factors using the DNN algorithm. The output variable for data collection is the number of losses caused by railway accidents over the last 21 years as collected by the Railway Safety Information System (RSIS) of the Ministry of Land, Infrastructure and Transport, while weather information at the time of the accident was collected from the KMA based on the location of the accident as input variables. An optimal model was developed for learning the collected output variable and input variables while fine-tuning network scenarios and hyperparameters by a trial-and-error process. To verify the model, it was first confirmed that the problem of overfitting was avoided by likening the prediction outcomes of the verification data and the test data, after which the DNN model was verified through comparison and verification with the MRA model. As a consequence of the comparative verification with the two models, the DNN model showed a 72.8% lower prediction error in the MAE and a 42% lower prediction error in the RMSE than the MRA model.