## 3 Results
### Comparative Analysis of the Forecasting Performances of the Different Models  
To evaluate the performances of the three ML models (BPNN, PLSR, and SVR) in predicting the soil cohesion and internal friction angle, we analytically assessed these models by using the evaluation indices \(R_{V}\)\({}^{2}\), \(RMSE_{V}\), and \(RE_{V}\). Figure 16 shows a comparison of \(R_{V}\)\({}^{2}\) and \(RMSE_{V}\).  
As can be seen from Figure 16, the \(R_{V}\)\({}^{2}\) of cohesion of the three ML models all reached above 0.8, showing good predictive ability, but BPNN exhibits the best prediction accuracy because its \(R_{V}\)\({}^{2}\) is the highest and its \(RMSE_{V}\) is the smallest (Figure 16). Although the \(RMSE_{V}\) of the PLSR value is lower than that of SVR, its \(R_{V}\)\({}^{2}\) value is lower than that of SVR, and its \(R_{C}\)\({}^{2}\) is only 0.76 (Figure 13), indicating that the correlation with the PLSR model is not good enough; therefore, we believe that SVR has a better ability to predict cohesion than PLSR. In conclusion, the predictive performance of the three ML models for cohesion is BPNN > SVR > PLSR.  
For the internal friction angle, among the three ML models, only the SVR model had an \(R_{V}\)\({}^{2}\) value of greater than 0.8, and SVR has the lowest \(RMSE_{V}\) with a value of 2.4, so the SVR model has the best performance. Both the \(R_{V}\)\({}^{2}\) and \(RMSE_{V}\) values of BPNN are superior to those of PLSR, so its internal friction angle prediction performance is second  
Figure 16: Comparison of different indices. (**a**) Comparison of R2; (**b**) comparison of RPD.  
Figure 15: Soil shearing parameters prediction of SVR models. (**a**) Soil cohesion; (**b**) soil internal friction angle.  
only to that of SVR; therefore, the prediction ability of the internal friction angle by the three ML models is SVR > BPNN > PLSR.  
Figure 17 illustrates the \(RE_{V}\) curves of different ML models, which compare the prediction deviations of the models.  
According to the results, for soil cohesion (Figure 17a), the \(RE_{V}\) of the BPNN, PLSR, and SVR models fluctuate within a range of 0-1.91, 0-2.31, and 0-3.90, respectively. The \(RE_{V}\) fluctuation range of the BPNN model is the narrowest, verifying that BPNN can more precisely predict soil cohesion than the other two models. As for internal friction angle (Figure 17b), the \(RE_{V}\) of the BPNN, PLSR, and SVR models varies from 0-0.67, 0-0.75, and 0-0.65, respectively. The fluctuation of the BPNN model is similar to that of the SVR model, but the SVR is slightly better than the BPNN model, and both of them are better than the PLSR model. Thus, we believe that the BPNN model is the best at predicting soil cohesion, and the SVR model is the most suitable for predicting the internal friction angle.  
In addition, Figure 17a shows that samples 5 and 11 used for predicting soil cohesion have \(RE_{V}\) values that significantly deviate from those of the other samples in all three ML models. As can be seen from Figure 17b, samples 6 and 18, which were used to predict the angle of internal soil friction, show similar deviations. By checking the values of validation set samples, we found that the water content of sample 5 was the greatest of all samples, sample 11 showed the greatest cone tip resistance, sample 6 had the lowest cohesion, and the internal friction angle of sample 18 was the least of all the samples. This indicates that when the parameter value is the lowest or highest value, the prediction reliability of the model decreases.  
Figure 17: \(RE_{V}\) change curves of different models. (**a**) Cohesion; (**b**) internal friction angle.