## 4 Analysis of Evaluation Results  
### Results of the Vulnerability Evaluation  
After grading the evaluation factors, the graded information values corresponding to the 13 evaluation factors and the superimposed total information values are obtained. Since the evaluation factors have different scales, distribution spaces, and values, the evaluation factors are normalized to unify the data to facilitate better data processing by the model. The commonly used method for normalization is min-max, which is calculated by the formula:  
\[X^{*}=(X-Xmin)/(Xmax-Xmin),\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation Factors** & **Classification** & **Type** & \(N_{ij}\) & \(S_{ij}\) & \(S_{ij}\)/\(S\) & \(N_{ij}\)/\(N\) & \(FR\) \\ \hline \multirow{4}{*}{Distance from faults} & 0–300 m & & 517,925 & 161 & 13.17\% & 15.71\% & 1.19 \\  & 300–600 m & & 472,810 & 110 & 12.03\% & 10.73\% & 0.89 \\  & 600–900 m & & 416,287 & 66 & 10.59\% & 6.44\% & 0.61 \\  & 900–1200 m & & 362,051 & 81 & 9.21\% & 7.90\% & 0.86 \\  & \textgreater{}1200 m & & 2,162,741 & 607 & 55.01\% & 59.22\% & 1.08 \\ \hline \multirow{4}{*}{Line of equations} & IX & & 284,748 & 64 & 7.24\% & 6.24\% & 0.86 \\  & VIII & & 560,974 & 96 & 14.27\% & 9.37\% & 0.66 \\  & VII & 1,456,770 & 407 & 37.05\% & 39.71\% & 1.07 \\  & VII outside (10 km) & & 1,629,322 & 458 & 41.44\% & 44.68\% & 1.08 \\ \hline \multirow{4}{*}{Rate of deformation of the descending rail} & \(<\)\(-\)50 mm/y & & 53,117 & 2 & 1.35\% & 0.20\% & 0.14 \\  & \((-50,-30]\) mm/y & & 98,026 & 2 & 2.49\% & 0.20\% & 0.08 \\  & \((-30,-10]\) mm/y & & 632,192 & 68 & 16.08\% & 6.63\% & 0.41 \\  & \((-10,-5]\) mm/y & continuous & 558,989 & 106 & 14.22\% & 10.34\% & 0.73 \\  & \((-5,5]\) mm/y & & 1,376,991 & 419 & 35.02\% & 40.88\% & 1.17 \\  & (5,10] mm/y & & 489,349 & 200 & 12.45\% & 19.51\% & 1.57 \\  & \textgreater{}10 mm/y & & 723,150 & 228 & 18.39\% & 22.24\% & 1.21 \\ \hline \multirow{4}{*}{Rate of deformation of the ascending rail} & \(<\)\(-\)50 mm/y & & 1534 & 5 & 0.04\% & 0.49\% & 12.50 \\  & \((-50,-30]\) mm/y & & 28,609 & 4 & 0.73\% & 0.39\% & 0.54 \\ \cline{1-1}  & \((-30,-10]\) mm/y & & 489,548 & 106 & 12.45\% & 10.34\% & 0.83 \\ \cline{1-1}  & \((-10,-5]\) mm/y & continuous & 528,806 & 110 & 13.45\% & 10.73\% & 0.80 \\ \cline{1-1}  & \((-5,5]\) mm/y & & 1,851,891 & 475 & 47.10\% & 46.34\% & 0.98 \\ \cline{1-1}  & (5,10] mm/y & & 563,188 & 204 & 14.32\% & 19.90\% & 1.39 \\ \cline{1-1}  & \textgreater{}10 mm/y & & 468,238 & 126 & 11.91\% & 12.29\% & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._where _Xmax_ and _Xmin_ represent the maximum and minimum values of the dataset. In this study, 13 evaluation factors are selected to establish a sample dataset, which includes landslide samples and non-landslide samples. A binary classification model was constructed to assign a value of \"0\" to the non-landslide unit and a value of \"1\" to the landslide unit. The sample data were divided into training dataset and test dataset according to 7:3. The training dataset was used for modeling operations, and the test dataset was used for model accuracy testing. In this study, we used python3.8 software to train the random forest model, import the samples into the software, divide the training dataset, and test the dataset by using the random forest classifier, the training dataset for modeling, to optimize the model before modeling, and search for the optimal hyper-parameter values by using the Bayesian optimization algorithm, which mainly focuses on the hyper-parameters such as n_estimators, max_depths, n_estimators, max_depths, min_samples_splits, and max_features to obtain their optimal parameter values. The values of hyperparameters through 64 iterations are max_depth = 18, min_samples_split = 3, min_samples_leaf = 1, and max_features = 1. The parameters are inputted into the model, and the modeling operation is conducted to obtain the landslide susceptibility results.