# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 4 Results
### Considering Various Input Sequence Lengths  
To further investigate whether the SafeNet method can identify pre-earthquake disturbances with varying input sequence lengths and whether it can improve performance, datasets with the following input sequence length of consecutive observations were created (Table 1): 80 (DataSet 02), 70 (DataSet 03), 50 (DataSet 04), and 40 (DataSet 05) consecutive observations.  
Figure 6 depicts the ROC curves and MCC, F1 score, and accuracy bar plot curves for the datasets with varying input sequence lengths. Table 2 lists the classification performance metrics achieved using the SafeNet. In Table 2, it is revealed that the overall F1 scores vary from 0.812 to 0.846, and the MCC varies from 0.654 to 0.717 for various datasets; these values are also shown in Figure 6D, which shows a performance comparison of the results. It was illustrated that as the input sequence length fluctuated, the performance of the model changed as well, and the optimal performance was achieved using the dataset with an input sequence length of 70 consecutive observations (DataSet 03). According to the ROC curves shown in Figure 6A-C, SafeNet also offered a reasonable performance for each class when DataSet 03 was used. Based on these results, we could conclude that the length of the input sequence had an influence on the performance of the SafeNet model, and that the best performance was achieved with an input sequence of 70 consecutive observations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **DataSet** & **MCC** & **F1** & **Accuracy** & \begin{tabular}{c} **AUC of** \\ **Class 0** \\ \end{tabular} & \
\begin{tabular}{c} **AUC of** \\ **Class 1** \\ \end{tabular} &
\begin{tabular}{c} **AUC of** \\ **Class 2** \\ \end{tabular} \\ \hline \multirow{9}{*}{SafeNet} & DataSet 01 & 0.684 & 0.830 & 0.830 & 0.910 & 0.929 & 0.500 \\  & DataSet 02 & 0.654 & 0.825 & 0.825 & 0.894 & 0.927 & 0.539 \\  & DataSet 03 & 0.717 & 0.846 & 0.846 & 0.931 & 0.946 & 0.545 \\  & DataSet 04 & 0.690 & 0.829 & 0.829 & 0.899 & 0.907 & 0.500 \\  & DataSet 05 & 0.662 & 0.812 & 0.812 & 0.920 & 0.907 & 0.515 \\  & DataSet 06 & 0.653 & 0.805 & 0.805 & 0.881 & 0.871 & 0.534 \\  & DataSet 07 & 0.665 & 0.812 & 0.812 & 0.909 & 0.917 & 0.521 \\  & DataSet 08 & 0.659 & 0.809 & 0.809 & 0.912 & 0.919 & 0.500 \\  & DataSet 09 & 0.644 & 0.801 & 0.801 & 0.909 & 0.917 & 0.531 \\  & DataSet 10 & 0.657 & 0.809 & 0.809 & 0.913 & 0.921 & 0.505 \\  & DataSet 11 & 0.510 & 0.697 & 0.697 & 0.869 & 0.898 & 0.537 \\  & DataSet 12 & 0.517 & 0.706 & 0.706 & 0.860 & 0.883 & 0.518 \\  & DataSet 13 & 0.656 & 0.812 & 0.812 & 0.896 & 0.915 & 0.539 \\  & DataSet 14 & 0.661 & 0.835 & 0.835 & 0.875 & 0.916 & 0.522 \\  & DataSet 15 & 0.687 & 0.830 & 0.830 & 0.911 & 0.907 & 0.530 \\  & DataSet 16 & 0.665 & 0.819 & 0.819 & 0.911 & 0.925 & 0.523 \\  & DataSet 17 & 0.657 & 0.814 & 0.814 & 0.908 & 0.926 & 0.545 \\ \hline CNN & DataSet 03 & 0.635 & 0.825 & 0.825 & 0.859 & 0.908 & 0.520 \\ \hline LSTM & DataSet 03 & 0.643 & 0.824 & 0.824 & 0.880 & 0.923 & 0.520 \\ \hline DNN & DataSet 03 & 0.660 & 0.834 & 0.834 & 0.890 & 0.930 & 0.509 \\ \hline GBM & DataSet 03 & 0.613 & 0.813 & 0.813 & 0.882 & 0.923 & 0.538 \\ \hline RF & DataSet 03 & 0.450 & 0.742 & 0.742 & 0.836 & 0.876 & 0.519 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of SafeNet and benchmark classifiers on different datasets.