# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 4 Discussion
### Practical Applications and Broader Impact  
The ILB model exhibits exceptional generalization capabilities, particularly in handling continuously updated datasets. Its incremental learning feature enables the model to effectively process static datasets traditionally used for slope stability prediction while also adapting to dynamic environmental data. In practical applications, as new slope monitoring data becomes available, the ILB model can quickly update its predictions without requiring full retraining, as conventional batch learning models do. In dynamic monitoring scenarios, such as mining areas, high-risk landslide zones, or infrastructure projects, the ILB model can provide real-time slope stability assessments and timely disaster warnings.  
Moreover, the ILB model holds significant cross-domain applicability. Its unique incremental learning mechanism can be extended to other fields that require rapid responses and real-time data processing, such as climate change forecasting, geological hazard monitoring, and infrastructure health management. By expanding its use across various domains, the ILB model can offer accurate and efficient predictions for diverse environments, effectively supporting decision-makers in emergency response and risk assessment.  
The increasing global challenges of climate change and human activities exacerbate the issue of slope instability. In particular, landslides and slope failures in mountainous regions, mining zones, urban infrastructure, and transportation networks pose serious threats to life and property safety. Reports indicate that many regions, especially in Asia, Latin America, and Africa, face frequent geological disasters, with numerous slopes in these areas in danger. Effective slope stability prediction and disaster prevention measures are urgently needed. The ILB model, through its efficient incremental learning and real-time data updates, offers dynamic monitoring and early warning of slope stability for these high-risk areas. It helps reduce the frequency and scale of disasters, minimizing casualties and economic losses.  
Globally, the ILB model not only supports regional slope stability prediction but also provides a new approach for disaster risk management. International cooperation, data sharing, and real-time early warning systems will further enhance global capacity to address slope-related disasters. By promoting the ILB model worldwide, we can provide technical support to more regions, helping governments and relevant departments better  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & \(H(m)\) & \(\beta(^{\circ})\) & \(\gamma(\text{kN/m}^{3})\) & \(C(\text{MPa})\) & \(\varphi(^{\circ})\) & \(ru\) & \
\begin{tabular}{c} **Slope** \\ **State** \\ \end{tabular} &
\begin{tabular}{c} **Predicted Stability** \\ **Probability** \\ \end{tabular} \\ \hline
1 & 4 & 35 & 12 & 0 & 30 & 0.25 & Stable & 9.07\% \\
2 & 50 & 45 & 20 & 20 & 36 & 0.5 & Failure & 64.68\% \\
3 & 21.19 & 22 & 19.63 & 11.97 & 20 & 0.4 & Failure & 23.85\% \\
4 & 330 & 49 & 25 & 48 & 40 & 0.25 & Stable & 98.58\% \\
5 & 443 & 47 & 25 & 46 & 35 & 0.25 & Stable & 99.75\% \\
6 & 299 & 45 & 25 & 55 & 36 & 0.25 & Stable & 96.99\% \\
7 & 393 & 46 & 25 & 46 & 35 & 0.25 & Stable & 99.32\% \\
8 & 8 & 33 & 24 & 0 & 40 & 0.3 & Stable & 77.33\% \\
9 & 10 & 45 & 22.4 & 10 & 35 & 0.25 & Stable & 74.00\% \\
10 & 61 & 20 & 21.43 & 0 & 20 & 0.5 & Failure & 26.76\% \\
11 & 12.8 & 28 & 21.82 & 8.62 & 32 & 0.49 & Failure & 56.76\% \\
12 & 252 & 45.57 & 29.16 & 9.2 & 55.33 & 0.2 & Stable & 98.40\% \\
13 & 216 & 41.75 & 29.16 & 9.2 & 55.33 & 0.2 & Stable & 97.49\% \\
14 & 296 & 45.89 & 29.16 & 9.2 & 55.33 & 0.2 & Stable & 99.07\% \\
15 & 312 & 30.05 & 29.16 & 9.2 & 55.33 & 0.2 & Stable & 98.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Verification results for the slope cases.  
cope with geological hazards and improving global disaster prevention and mitigation capabilities.