# Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm
## 5 Conclusions  
This article focuses on the problem of water innyh in water-rich tunnels. Through the investigation and analysis of a large number of accident cases and by processing the associated data, a hazard prediction model for water innyh in water-rich tunnels based on RF is established. Finally, the model is validated by taking the water innyh accident in Yuanliangshan tunnel as an example, and the main conclusions are as follows:  
1. Through the analysis of a large number of cases of water innyh accidents, indicators suitable for the hazard assessment of water innyh in water-rich tunnels were selected, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accident Section** & \begin{tabular}{c} **Actual Hazard** \\ **Grades** \\ \end{tabular} & \
\begin{tabular}{c} **Predicted Hazard** \\ **Grades** \\ \end{tabular} &
\begin{tabular}{c} **Comparison between Predicted** \\ **Results and Actual Results** \\ \end{tabular} \\ \hline Section 1 & 4 & 4 & Consistent \\ Section 2 & 4 & 4 & Consistent \\ Section 3 & 4 & 4 & Consistent \\ Section 4 & 2 & 4 & Inconsistent \\ Section 5 & 3 & 3 & Consistent \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the actual hazard level and the predicted hazard level. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Accident Section** & \begin{tabular}{c} **Stratigraphic** \\ **Lithology** \\ \end{tabular} & \begin{tabular}{c} **Inadequate** \\ **Geology** \\ \end{tabular} & \begin{tabular}{c} **Rock Dip** \\ **Angle** \\ \end{tabular} & \begin{tabular}{c} **Negative Terrain** \\ **Area Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Surrounding** \\ **Rock Grade** \\ \end{tabular} &
\begin{tabular}{c} **Hydrodynamic** \\ **Zonation** \\ \end{tabular} \\ \hline Section 1 & 0.8 & 0.9 & \(-0.7\) & \(0.1\) & \(1.55\) & \(-1.81\) \\ Section 2 & 0.8 & 0.9 & \(-0.96\) & \(0.1\) & \(1.55\) & \(-1.81\) \\ Section 3 & 0.8 & 0.9 & \(-0.05\) & \(1.55\) & \(1.55\) & \(0.76\) \\ Section 4 & 0.8 & \(-0.29\) & \(-0.44\) & \(-0.32\) & \(1.55\) & \(-1.81\) \\ Section 5 & \(-0.34\) & \(-0.29\) & \(-0.09\) & \(0.29\) & \(1.55\) & \(-0.53\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Preprocessed data. Figure 9: Implementation process of predicting water innyh in Yuanliangshan tunnel. an evaluating indicator system was constructed, and each classification of each evaluating indicator was obtained. The classification criteria for the hazard level of water inrush in water-rich tunnels in this article were established. 2. After ten-fold cross validation and optimization of the hyperparameters, the AUC values of the four hazard levels in the ROC curve are all above 0.98. Further comparison was made between the RF model and the SVM model, and it was found that the prediction accuracy of the RF model reached 98%, which is significantly better than the 87% of the SVM. This indicates that the RF model has high applicability when predicting the hazard of water inrush in water-rich tunnels. 3. Taking the water inrush accident in the Yuanliangshan tunnel as an example, the accuracy of the RF algorithm-based hazard prediction model of water inrush in a water-rich tunnel was verified. The results show that the established model has strong applicability and high accuracy when predicting water inrush in a water-rich tunnel, and can be applied to practical engineering when identifying water inrush hazards in a water-rich tunnel. At present, this study has the disadvantage of having a small number of data samples, which leads to a relatively vague classification of the degree of danger of each accident. By supplementing a large number of accident cases and conducting regression analysis on them, the harm caused by sudden water inrush in tunnels can be more accurately predicted. The collected data also have a large number of missing situations, and more comprehensive data collection is the key to improving prediction accuracy.