# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 2 Materials and Methods  
### Data and Features  
The determinants of slope stability are classified into geometric parameters of the slope, soil properties, and external influences. These components collectively dictate the stability of slopes. The fundamental geometric attributes encompass slope height (\(H(\text{m})\)) and overall slope angle (\(\beta(^{\circ})\)), whereas the primary soil parameters include unit weight (\(\gamma(\text{kN}/\text{m}^{3})\)), cohesion (\(C(\text{kPa})\)), and internal friction angle (\(\varphi(^{\circ})\)). To address the impact of external variables, this study adopted the pore-water ratio (\(ru\), the proportion of pore water pressure to overburden pressure) as one factor. Consequently, six pivotal factors were identified in this research that influence slope stability: slope height (\(H\)), slope angle (\(\beta\)), unit weight (\(\gamma\)), cohesion (\(C\)), angle of internal friction (\(\varphi\)), and pore water ratio (\(ru\)).  
To validate these indicators, the study compiled selections of slope stability influences from the relevant literature, as shown in Table 1. Table 1 shows that most of the literature selects the above parameters as the principal factors influencing slope stability. As shown in Table 1, in addition to the six factors selected in this study, factors such as Soil Depth and Rainfall Intensity have also been identified by some scholars as influencing factors. Although these factors may affect slope stability, our primary focus is on their prevalence, quantifiability, and theoretical foundation in slope stability research. For example, slope height and angle directly determine slope stability, unit weight, cohesion, and internal friction angle, which are core parameters influencing soil strength, while pore water ratio reflects the impact of moisture on soil mechanical properties. In addition, considering the difficulty in accurately obtaining data for factors such as Peak Ground Acceleration and Rainfall Intensity, including them in the study would reduce the database size, thereby impacting the model's accuracy. Therefore, to ensure model stability and evaluation accuracy, we selected these six factors and validated their effectiveness in slope stability prediction based on existing literature. In the future, with improved data acquisition methods, further exploration of the potential contribution of other influencing factors to model performance can be undertaken.  
To develop predictive models and enhance the reliability of the findings, this study compiled 242 slope cases from the literature into a comprehensive slope database that incorporates the six determinants and includes the slopes' stability status and geographical data [7; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. The data used in this study is sourced from previously published literature. To reduce selection bias in the dataset, we made an effort to include slope cases from diverse geographic locations and various research methodologies. Additionally, we aimed to balance the number of stable and unstable slopes. In terms of stability, the database encompassed 139 stable and 103 unstable slopes. Geographically, it included 190 slopes within China and 52 from other regions.