# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
## 3 Methods
### Decision Tree  
In this study, two decision tree methods were used to transform the liquefaction probability equation into tabular form. These included an importance ranking method which is called variable importance, and the results obtained by this method were used as the basis for selecting evaluation parameters. The application of related methods in liquefaction evaluation is rare and needs further study.  
A decision tree is a branch of machine learning that represents a mapping relationship between object attributes and object values. This relationship can be graphically displayed in a tree shape and intuitively understood as a prediction model of target values. Based on trial calculations, two decision trees--a chi-squared automatic interaction detector (CHAID) tree and a classification and regression tree (CRT)--obtained good results in this study. In the calculation process, there are two kinds of target variables. When the importance of the variable is sorted, the target variable specifies whether liquefaction occurred. When constructing the liquefaction possibility scale, the target variable is the pre-classification of the liquefaction probability. All the decision trees were developed using SPSS ver. 22. Below is a brief introduction of CHAID and CRT.  
CHAID is a decision tree that uses a multipath split based on a target variable, and each independent variable forms corresponding contingency tables [20; 21]. The samples are optimally split based on the chi-squared significance test, and finally, a classification rule in the form of a multi-fork tree is formed. CRT is the other algorithm used to build decision trees in this study [22]. Its purpose is to divide the target variables accurately. When dividing the variables, the training dataset composed of test variables and target variables is cyclically dichotomized, and finally, a binary decision tree is constructed. The algorithm can be used for both the classification of discrete variables and the regression of continuous variables. After trial calculations, it was found that the combination of these two methods could effectively classify the potential grade of liquefaction.  
The variable importance module in the CRT algorithm was used to quantitatively evaluate how the parameters influenced the liquefaction in this study. In the process of tree growth, the total dataset is divided into many subsets of different independent variables. The role of the variables in this segmentation is the variable importance (_VI_), which can measure how much effect a variable has in a particular tree. The variable importance _VI_(X) of predictor variable X is expressed in terms of a normalized quantity relative to the variable having the largest measure of importance. It ranges from 0 to 100, with the variable having the largest measure of importance scored as 100. The results of this analysis are presented in Section 4.  
In the process of generating the decision tree in this study, the target variable is the liquefaction potential, and the test variable represents the influence parameters of liquefaction, such as the intensity of the possible ground motion and the information about the saturated liquefiable soil layer. The influence of the parameters on the occurrence ofliquefaction is described quantitatively by the variable importance, and the rules of the decision tree are used to predict the possible categories of liquefaction. The results of this analysis are presented in Section 6.