## 3 Sensitivity Analysis of Influencing Factors
### Feature Importance Ranking
As shown in Figure 10i, in the results obtained from the two tests, the first type of parameters included A10 (tunnel buried depth), A6 (tunnel diameter), and A3 (geotechnical weight); and the third type of parameters included A7 (diameter of the karst cave), A4 (cohesion), and A5 (internal friction angle). Figure 10: _Cont._  
The traditional method for the sensitivity analysis of influencing factors of karst tunnel construction is the range analysis method based on the orthogonal test design. The  
Figure 10: The ranking of feature importance: (**a**) the first test on vertical displacement; (**b**) the second test on vertical displacement; (**c**) vertical displacement; (**d**) the first test on first principal stress; (**e**) the second test on first principal stress; (**f**) first principal stress; (**g**) the first test on principal stress difference; (**h**) the second test on principal stress difference; (**i**) principal stress difference. range analysis method has the advantages of simple calculation and intuitive expression. However, there is also a disadvantage of misjudging the true dispersion degree of data due to sensitivity to outliers. And the random forest algorithm has the advantages of high accuracy and reducing the risk of overfitting by introducing randomness. Furthermore, it could clearly select the features with the greatest importance after training the random forest model. Compared with the range analysis method based on the orthogonal experimental design, the random forest algorithm could avoid the misjudgement of the true dispersion degree of the dataset caused by the abnormal value of the dataset. Table 6 is the ranking table of the importance of the construction characteristics of karst tunnels based on the orthogonal test design and random forest algorithm. Table 5 is the ranking table of influencing factors of karst tunnel construction based on the orthogonal test design and range analysis method. Due to the randomness of the random forest algorithm, it has been reflected in two aspects: sample randomization and feature randomization. Therefore, the feature importance ranking of karst tunnel construction obtained by running the random forest algorithm each time is not exactly the same. Two representative calculation results which could fully reflect the feature importance of karst tunnel construction would be selected. As shown in Tables 4 and 5, among the two test results obtained by applying the orthogonal test design and random forest algorithm with vertical displacement as the target variable, the three influencing factors with the greatest feature importance were A6 (tunnel diameter), A10 (tunnel buried depth), and A11 (exexavation dimension). In the calculation results obtained by the orthogonal experimental design and range analysis method, the three influencing factors with the highest sensitivity were A6 (tunnel diameter), A10 (excavation dimension), and A1 (elastic modulus). However, in the analysis of variance (ANOVA), the results observed in A6 (tunnel diameter) and A10 (tunnel buried depth) were statistically significant. In other words, whether A6 (tunnel diameter) and A10 (tunnel buried depth) are the two most sensitive factors remains to be further determined. The random forest algorithm of orthogonal experimental design can solve this problem well, and it also verified that A6 (tunnel diameter) and A10 (tunnel buried depth) were indeed the two most sensitive factors. When taking the first principal stress as the target variable, the two test results obtained by the orthogonal test design and random forest algorithm show that the two most important influencing factors were A10 (tunnel buried depth) and A9 (location of karst cave). In the calculation results achieved by the orthogonal experimental design-range analysis method, the two most sensitive influencing factors were also A10 (tunnel buried depth) and A9 (location of karst cave).