# Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4
## 4 Water Inrush Risk Assessment
### Evaluation Index System for Water Inrush Risk
The training prediction results were completely consistent with the training samples, as shown in Table 7, indicating that the model met the training requirements. Three parameters of different mileage in the static sediment interval were selected as the test sample data. According to the geological survey report and construction site data, the risk index parameters of water inrush in the static sand section were obtained through sorting, as shown in Table 8. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Quantitive Index Value** & **I\({}_{\text{11}}\)** & **I\({}_{\text{12}}\)** & **I\({}_{\text{13}}\)** & **I\({}_{\text{14}}\)** & **I\({}_{\text{15}}\)** & **I\({}_{\text{16}}\)** & **I\({}_{\text{21}}\)** & **I\({}_{\text{22}}\)** & **I\({}_{\text{23}}\)** & **I\({}_{\text{31}}\)** & **I\({}_{\text{32}}\)** & **I\({}_{\text{33}}\)** & **I\({}_{\text{34}}\)** \\ \hline Correlation & 0.848 & 0.862 & 0.925 & 0.675 & 0.923 & 0.878 & 0.831 & 0.825 & 0.900 & 0.892 & 0.817 & 0.718 & 0.832 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Index correlation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Number** & **Sample** & **Grade** & **I\({}_{\text{11}}\)/m** & **I\({}_{\text{12}}\)/m** & **I\({}_{\text{33}}\)** & **I\({}_{\text{14}}\)/MPa** & **I\({}_{\text{15}}\)** & **I\({}_{\text{16}}\)** & **I\({}_{\text{21}}\)** & **I\({}_{\text{22}}\)** & **I\({}_{\text{23}}\)** & **I\({}_{\text{31}}\)** & **I\({}_{\text{32}}\)** & **I\({}_{\text{33}}\)** & **I\({}_{\text{34}}\)** \\ \hline Kaisheng & 1 & 1 & 6.2 & 12.6 & whole & 44.23 & underdeveloped & II & 41.35 & flat & 39.68 & slightly & & \\ Sec- & & & & & & & & & & & & & & & \\ tion of & 2 & II & 6.2 & 12.8 & broken & 44.23 & more & & & & & & & & & \\ Qing- & 3 & IV & 6.2 & 14 & extremely & 11.97 & development & V & 77.21 & flat & 85.7 & rich in & 12 & 4.4928 & 59.5 \\ Metro & & 12 & 4 & IV & 6.2 & 13.95 & extremely & 10.8 & very & & & & & & & & \\ Line 1 & 4 & IV & 6.2 & 13.95 & broken & 137.3 & more & & & & & & & & & \\  & 5 & II & 5.2 & 11.6 & broken & 137.3 & developed & IV & 11 & slope & 34.48 & slightly & & & \\ section of & 6 & IV & 5.2 & 11.5 & extremely & 28.2 & very & & & & & & & & & \\ Qing- & 7 & II & 5.2 & 11 & broken & 28.2 & very & & & & & & & & & \\ Metro & & & & & & & & & & & & & & & & & \\ Line 2 & 8 & II & 5.2 & 10.4 & broken & 137.3 & more & & & & & & & & & & \\  & & & & & & & & & & & & & & & & & \\ \hline Shimiao & 9 & IV & 6.4 & 13.6 & broken & 15.6 & development & VI & 77.43 & slope & 94.12 & rich in & 9.73 & 15 & 57.9 \\ sec- & & & & & & & & & & & & & & & & & \\ tion of & 10 & III & 6.4 & 16 & broken & 26 & more & & & & & & & & & & & \\ Qing- & 3 & & & & & & & & & & & & & & & & \\ Gao & 11 & I & 6.4 & 9.8 & whole & 57.3 & underdeveloped & IV & 1.63 & denoudatiq & 92.65 & variety & 0.16 & 0.01 & 57.9 \\ Metro & & & & & & & & & & & & & & & & & \\ Line 2 & 12 & IV & 6.4 & 15.6 & extremely & 6.5 & very & & & & & & & & & & \\  & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training sample data. Three groups of test samples were evaluated for water inrush risk level, and the same data, which had been processed, was used to evaluate water inrush risk level through a traditional RBF neural network and BP neural network. The evaluation results are shown in Table 9.  
It can be seen from Table 8 that the risk level of water inrush risk assessment results. Qingdao Metro Line 4 predicted by the model was completely consistent with the actual risk level, indicating that the results of the model were reliable. In addition, compared with the traditional RBF neural network model and the BP neural network model, this model had higher prediction accuracy, better performance, smaller mean square error and did not easily to fall into the local optimal solution, indicating that the improved RBF neural network model could better fit the complex relationship between the risk level of water inrush and various factors.