## 6 Conclusions  
This study took the No. 1 tunnel of the main canal in the Xiaolangdi North Bank Irrigation Area as an example, and conducted inversion of its soil parameters and water level based on numerical simulation and back-analysis of BP neural network. The following conclusions were drawn:  
1. By numerically simulating the effects of changes in groundwater level and soil parameters on displacement, it was found that groundwater level and soil parameters have a significant impact on the deformation of tunnel surrounding rocks. As the groundwater level rises, the horizontal direction (X direction) displacement of the tunnel surrounding rocks gradually decreases, while the vertical direction (Z direction) displacement gradually increases. With the increase of soil cohesion and internal friction angle, both the horizontal direction (X direction) and vertical direction (Z direction) displacements show a trend of approximate linear decrease. 2. Through sensitivity analysis of the tunnel parameters, the main influencing parameters and secondary influencing parameters that conform to the actual situation of the project were determined: changes in water level had the most significant impact on surface deformation, followed by the internal friction angle, and the least sensitive was the cohesion. \begin{table}
\begin{tabular}{c c c} \hline
**Measured Displacement/m** & **Calculated Displacement/m** & **Absolute Error/m** & **Relative Error/\%** \\ \hline
0.5 & 0.548 & 0.048 & 9.6 \\ \hline \end{tabular}
\end{table} 
Table 7: Analysis of error between calculated and measured displacement at monitoring points. Figure 9: Contour plot of the forward calculation of vertical displacement. 3. Parameter inversion analysis provided the following values: cohesion (\(c_{1}\)) for loess 4 is 53.346 kPa with an internal friction angle (\(\varphi_{1}\)) of 29.129\({}^{\circ}\), cohesion (\(c_{2}\)) for loess 4-1 is 30.838 kPa with an internal friction angle (\(\varphi_{2}\)) of 14.716\({}^{\circ}\), and cohesion (\(c_{3}\)) for loess 5 is 22.189 kPa with an internal friction angle (\(\varphi_{3}\)) of 3.163\({}^{\circ}\). The groundwater level elevation is 223.402 m. Comparison with field survey results indicated that the calculated values were generally within the specified range provided in the survey report, confirming the reliability of the results. 4. Comparing the predicted settlement values of the BP neural network with the settlement values calculated by finite element analysis, the absolute error is 0.048 m, and the relative error is 9.6%. The calculation accuracy meets the requirements, and the method has a relatively small relative error compared with the field measurement values. The simulation results are consistent with the actual situation, which verifies the rationality and effectiveness of the method. Conceptualization, Z.Z. ; Methodology, Y.L., J.D. and B.W. ; Software, C.W. ; Investigation, B.W. ; Resources, Y.L. and Z.Z. ; Data curation, B.W. ; Writing--original draft, C.W. ; Writing--review & editing, C.W. ; Visualization, Z.Z. and C.W. ; Supervision, Y.L. and J.D. ; Project administration, J.D. ; Funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript. This research work is sponsored by the Henan Province 2022 Water Science and Technology Project (Grant No. GG202268), the Key Research and Development Project of Henan Province (Grant No. 24111322900), the National Key Research and Development Project of China (Grant Nos. 2019 YFC1509704 and 2019 YFC1509701), the National Natural Science Foundation of China (Grant Nos. UI704243, 41741019 and 41977249), Henan Provincial Science and Technology Research Project (Grant Nos. 192102310006 and 232102320035), Key Research Projects of Higher Education Institutions in Henan Province (Grant No. 24A410003). Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.