# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 5 Conclusions  
Datasets are frequently updated in slope stability prediction. Traditional machine learning models require retraining and adjustments, which require considerable time and computational resources. This article has investigated the application of an ILB model to slope stability evaluation. An inventory of 242 slope cases in published literature was collected for ILB modeling. The performance of the developed ILB model was tested and compared with four conventional batch learning models: RF, GBM, SVM, and MLP. We drew the following conclusions:  
1. The developed ILB model demonstrates promising performance in slope stability evaluation. When a new dataset is introduced, the model swiftly updates and optimizes itself while maintaining its predictive capability on the original data, significantly improving the predictive performance of the new data.
2. Compared with conventional batch learning models, the accuracy of the ILB model improves significantly as the number of incremental learning iterations increases. This enhancement is roughly comparable to that of the RF model at the same stage but is notably higher than that of the GBM, SVM, and MLP models. Despite this, the ILB model exhibits significantly greater computational efficiency under identical conditions than the RF model. The generalization of the ILB model surpasses that of the RF, SVM, GBM, and MLP models, demonstrating the highest level of performance.
3. The relative variable importance and PDP analysis reveal that friction angle (\(\varphi\)), slope gradient (\(\beta\)), and pore water pressure coefficient (\(ru\)) exert the most significant influence on slope stability.
4. However, despite the remarkable and encouraging results, there are still some questions for future study. The effects of supporting structures and variations in slope parameters on slope stability merit further discussion. Furthermore, the dataset used in this study originates from published literature. The developed model can be improved by establishing a larger, more balanced, reliable dataset. Additionally, when a dataset is accessible, the model developed in this study can be recommended for application to similar classification problems.  
Conceptualization, S.Z., M.H. and Z.X.; methodology, S.Z. and W.H.; software, W.H.; validation, W.H. and J.L.; formal analysis, S.Z. and W.H.; investigation, J.Z. and M.H.; resources, J.Z., S.Z. and M.H.; data curation, W.H. and J.L.; writing\(-\)original draft preparation, W.H., Z.X., and J.L.; writing\(-\)review and editing, S.Z., W.H., and J.Z.; visualization, M.H., Z.X. and J.L.; supervision, S.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work has received funding from the Science and Technology Infrastructure Program of Guizhou Province (2020-4Y047), the Natural Science Foundation of Changsha (kq2402072), the Postgraduate Scientific Research Innovation Project of Hunan Province (CX20204032), the Opening Fund of the Key Laboratory of Geological Safety of Coastal Urban Underground Space, Ministry of Natural Resources (BHKF2023Y04), and the Science and Technology Project of Transportation and Communication Ministry of Guizhou Province (2023-312-030). All these fundings are greatly acknowledged.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.