# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 3 Database Establishment and Analysis
### Data Collection and Analysis  
When using the machine learning method to predict slope stability, the construction is very important, and a good construction can make the model learn more efficiently and get good prediction results more easily. In order to ensure this, nearly 500 sets of slope case data were collected from different countries and regions [7; 9; 10; 11; 12; 33; 34; 35; 36; 37]. After sorting out, the obtained basic slope parameter data is calculated to obtain the required characteristic data. In order to improve data quality, speed up model convergence, and improve model performance, data were preprocessed, outliers and noisy data were identified and processed based on the 3-sigma rule, and eigenvalues were scaled to the interval [0, 1] by linear normalization. The principle was as follows. Finally, the processed data were compiled into 458 datasets as input to the prediction model, part of the data set is shown in Table 1.  
\[x_{ij}^{\prime}=\frac{x_{ij}-min(x_{i})}{max(x_{i})-min(x_{i})} \tag{14}\]  
Table 2 shows the descriptive statistics of the dataset, and Figure 2 shows the distribution of box violins for six input features. It can be seen from Figure 2 that the distribution of shear strength, effective stress, and pore ratio presents an obvious bimodal distribution pattern, indicating that there are two main datasets, and most of the data are concentrated in a lower range, but a small part of the data are concentrated in a higher range. The density distribution is approximately uniform, but there is an obvious aggregation area at the medium density value, which is relatively smooth on the whole without obvious sharp peaks. The slope height distribution is unimodal, and most data are concentrated in the middle and high sections. The distribution of slope angles is relatively scattered, and the overall distribution of slope angles is more than most of the points in the medium range, indicating that the slope angles in this study area change greatly, but most of the cases remain in the moderate range. Based on shear strength, effective stress, and slope bulk density, it can be seen that the slope cases in the dataset include rock slope and soil slope. Based on the slope angle, it can be seen that the slope cases collected include gentle slope, medium slope, steep slope, and extremely steep slope. From the slope height distribution, it can be seen that the dataset contains low slope, medium slope, high slope, and ultrahigh slope. Through the comprehensive analysis of the different influencing factors of the slope, the composition of the dataset includes a variety of slope types, which is more comprehensive. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**NO.** & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\) & \(\mathbf{X_{5}}\) & \(\mathbf{X_{6}}\) & **F** \\ \hline
1 & 65.0015 & 173.6988 & 20.41 & 10.7 & 22 & 0.35 & 1.1802 \\
2 & 82.1680 & 192.8674 & 19.63 & 12.2 & 22 & 0.41 & 1.1677 \\
3 & 153.5715 & 231.9709 & 21.82 & 12.8 & 28 & 0.49 & 1.3067 \\
4 & 202.8551 & 871.1535 & 20.41 & 45.7 & 16 & 0.2 & 0.9386 \\
5 & 105.9062 & 156.8999 & 18.84 & 10.7 & 25 & 0.38 & 1.5978 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
458 & 1455.9911 & 2599.6168 & 21.5 & 123.6 & 41.5 & 0.36 & 0.6436 \\ \hline \end{tabular}
\end{table} 
Table 1: Partial dataset. By calculating and visualizing the correlation coefficients of the input dataset, the Kendall correlation coefficient matrix between the six features is obtained, as shown in Figure 3. As can be seen from the figure, there is a strong correlation between variables X\({}_{1}\), X\({}_{2}\), and X\({}_{4}\), that is, there is a strong positive correlation between shear strength, effective stress, and slope height, because slope height is the main linear factor affecting effective stress, and effective stress is the key to determine the shear strength.