# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 5 Experiments
### Ablation Experiments  
The data explored in this paper are actual data of tunnel construction personnel, whose anomaly data account for a small proportion of the total data volume. In the study of practical engineering problems, the data proportion directly affects the accuracy and  
Figure 10: Experimental results on dataset 3, where (**a**) shows the training set results and (**b**) shows the test set results. efficiency of the model. Accordingly, the data proportion and balance have a certain degree of influence on the model performance. In order to explore the relationship of this influence, we set the alarm state values in a certain proportion and explore the relationship of (5-50%) proportion of data on the model. The experimental data are dataset 1 (Xinjiang Kuitun River diversion tunnel project (second standard)), the RBF kernel function of the above four experiments is selected as the kernel function, and the accuracy of their test sets are counted at different proportions. The experimental results are shown in Figure 11, where Figure 11a-d represent human characteristic data, environmental characteristic data, human-environment characteristic data, and human-environment-location feature data. Figure 11: Experimental results. (**a**) represents human characteristic data; (**b**) represents environmental characteristic data; (**c**) represents human–environment characteristic data; (**d**) represents human–environment–location characteristic data. The horizontal axis represents the percentage of abnormal data, and the vertical axis represents the prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Human Body** \\ **Characteristics** \\ \end{tabular} & \multicolumn{2}{c}{**Environmental Characteristics**} & \multicolumn{2}{c}{\begin{tabular}{c} **Human–** \\ **Environmental** \\ **Characteristics** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Body–Environmental** \\ **Characteristics–Location** \\ **Information Characteristics** \\ \end{tabular} } \\ \hline Datasets & Type & \begin{tabular}{c} Number \\ of SVM \\ \end{tabular} & Accuracy & \begin{tabular}{c} Number \\ of SVM \\ \end{tabular} & Accuracy & \begin{tabular}{c} Number \\ of SVM \\ \end{tabular} & Accuracy & \begin{tabular}{c} Number of \\ SVM \\ \end{tabular} & Accuracy \\ \hline Dataset 1 & \begin{tabular}{c} Linear \\ kernel \\ RBF \\ kernel \\ \end{tabular} & 59 & 94\% & 61 & 91\% & 70 & 94\% & 76 & 96\% \\ \hline Dataset 2 & \begin{tabular}{c} Linear \\ kernel \\ RBF \\ kernel \\ \end{tabular} & 72 & 98\% & 79 & 99\% & 271 & 93\% & 301 & 91\% \\ \hline Dataset 3 & \
\begin{tabular}{c} Linear \\ kernel \\ RBF \\ kernel \\ \end{tabular} & 78 & 96\% & 81 & 95\% & 62 & 98\% & 46 & 93\% \\ \hline Dataset 4 &
\begin{tabular}{c} Linear \\ kernel \\ RBF \\ kernel \\ \end{tabular} & 69 & 99\% & 49 & 99\% & 214 & 94\% & 242 & 95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of personnel safety warning model. Comparing the experiments with different warning state data ratios, we can see that the prediction accuracy of each type of data is above or below 90% at different warning state ratios, and the classification state is better than other ratios when the data ratio is below 15%. Figure 11d shows that the OCSVM model with the addition of location information can improve the performance of the tunnel staff safety situation early warning, and the accuracy is above 90% for all of them. Then, in the same experimental conditions, the four cases were compared in a cross-sectional manner, setting the proportion of abnormal data to 15%, selecting the kernel function to choose, and training 100 batches on dataset 1.