# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
## 6 Criteria Table of Liquefaction Potential Grade
### Obtaining Criteria through Decision Tree Method  
In the first stage, the CHAID classification tree method was adopted, which is a common decision tree method in machine learning, and SPSS was selected as the analysis software. After several parallel calculations, the tree results that violated the natural laws of soil liquefaction were excluded. In the growth of the tree, the maximum depth was set to two, the minimum number of parent nodes was set to ten, and the number of child nodes was set to ten. The tree result shown in Figure 4 was selected as the final result, which was consistent with the physical soil liquefaction phenomena and adequately subdivided the data. The output of the tree involved two parameters: \(V_{s}\) and \(a_{\max}\). Figure 4: Decision tree for shear wave velocity liquefaction potential grades. Figure 3: Histogram of \(P_{L}\) versus data frequency for preliminary classification. In the first layer of the tree, the shear wave velocity was taken as the separation parameter, and the database was divided into four parts according to the three cut-off values of the shear wave velocity: 117.6 m/s, 160.7 m/s, and 195.0 m/s. In these four parts, the ground motion acceleration was taken as the separation parameter, and the tree continued to grow. Finally, the tree had eleven final nodes. The purity was relatively high. Therefore, in this study, the result of this tree was chosen as the basis for establishing the classification rules of the liquefaction potential for the three grades. The tree did not include \(d_{s}\), \(d_{w}\), or other parameters commonly used in existing liquefaction evaluation methods, as \(d_{s}\) and \(d_{w}\) were not of high importance in the liquefaction classification. After statistical analysis, it was found that the \(d_{w}\) values for all the sites in the database were less than or equal to \(d_{s}\); that is, all sites were saturated and had reached the precondition for liquefaction to occur in the saturated soil layer. Liquefaction can occur only when other indices, such as the ground motion intensity, reach certain values. Therefore, the method constructed in this study is suitable for the liquefaction discrimination of saturated soil, and the premise of the method is that the groundwater level was less than the depth of the discriminated layer. The classification results of the tree described above were analyzed. The recall in Table 4 represents the percentages of true positive samples in the test set that were identified by the tree, and the precision is the percentage of true positive samples in the predicted set. It was found that the recall values of the three grades were all above 80%, especially for the separation of high-grade and low-grade data, for which the recall could approach or surpass 90%. This showed that the model had a good separation effect on these two subsets of the data. However, it is worth noting that the range of probabilities of liquefaction across the middle grade was large (20-80%), and the middle grade contained more than 50% of the total data volume. To further distinguish this part of the data (296 data points with'medium' actual values shown in Table 4), in this study, a variety of decision tree methods were attempted, and it was found that the CRT classification tree could distinguish this part of the data more effectively. The partition value was set to \(P_{L}\) = 50%. After the division limit was set to \(P_{L}\) = 50%, the CRT classification tree result for the further classification of this part of data is as shown in Figure 5.  
The recall values of the medium-low and medium-high grades in Table 5 were 83.46% and 71.6%, respectively, and the corresponding precision values of the two grades were 68.83% and 82.21%. This indicated that the model could identify the medium-low grade more accurately at the cost of identifying more medium-low-grade samples as medium-high, which showed the conservative degree of the model. In this paper, more than 70% of the data were correctly separated by the decision tree method.