# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 2 Materials and Methods
### Modeling Process of the Incremental Learning Bayesian Model  
Using six characteristic parameters (\(H\), \(\beta\), \(\gamma\), \(C\), \(\varphi\), and \(ru\)) as input and slope stability classification as output, an ILB model was constructed by combining incremental learning theory with a naive Bayesian model. A stable state of the slope in the dataset is indicated by 1, while an unstable state is indicated by 0. To compare the performance of incremental and batch learning models, this study employed the random forest (RF), gradient boosting machine (GBM), multilayer perceptron (MLP), and support vector machine (SVM) batch learning models alongside the ILB model.  
During the experiment, 242 slope cases were collected and assigned to one of the two datasets based on geographical location, Dataset I (190 cases) and Dataset II (52 cases). Subsequently, Dataset I was randomly divided into a training set (133 cases) and a test set (57 cases) in a 7:3 ratio to train and assess the classification ability of the models. The test set evaluated the classification performance of the trained models, while Dataset II assessed the model's generalization. Evaluation indices such as accuracy and AUC were used to measure the models' performance, as shown in Figure 1.  
Figure 1: Diagnostic model of prediction of slope stability state using ILB method.  
In the ILB, the initial model ILB0 was trained using the first case in the training set. Then, based on ILB0, the second case was used for incremental training to obtain ILB1. This incremental training process continued with the remaining 131 cases until all cases in the training set had been processed and a training round was completed. To minimize the impact of input order on the incremental learning model, the case order in the training set was randomized before each training round. The results of 10 rounds were averaged to construct the accuracy and AUC value curves. Dataset II was used to evaluate the generalization ability of the trained model.  
In the batch learning models, standardized input parameters underwent fivefold cross-validation for sampling within the training set. The grid search method then explored hyperparameters across the four batch learning models to identify the optimal hyperparameter combination. This combination was integrated into the model to produce trained models. For a more detailed comparison with the incremental learning model, the training set in batch learning models was sequentially divided into 10 groups, each containing 13 cases. Training started with the first group of cases for the initial training iteration, followed by a second iteration using the first and second groups, and so on, until all 10 groups had been trained, marking the completion of one training round. To ensure consistency with the experimental conditions of the incremental learning model, the case order in the training set was randomized before commencing the batch learning experiment. Ten rounds were scheduled, and the results were averaged to generate accuracy and AUC change curves. Additionally, the computational efficiency of the models was assessed by recording the time spent on each experiment.