# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 1 Introduction  
Slope failures are a global concern. Understanding and mitigating slope instability are becoming increasingly critical with the rising demands for land use, infrastructure development, and resource extraction in mountainous regions. Numerous incidents of slope instability occur worldwide, leading to significant casualties, property damage, and environmental impact [1]. Consequently, accurate slope stability prediction is paramount for mitigating potential hazards and ensuring the safety and sustainability of properties and communities. Slope stability prediction involves the analysis of factors contributing to slope failure, such as geological formations, soil properties, hydrological conditions, seismic activity, and human-induced alterations. The interplay of these factors often presents a challenging puzzle, requiring interdisciplinary approaches that integrate geological, geotechnical, hydrological, and geospatial data [2]. Numerous studies have explored different methodsand approaches to predict slope stability effectively [3]. In general, slope stability evaluation uses model-driven and data-driven methods [4,5]. Model-driven methods rely on mathematical and physical models to simulate the behavior of slopes under various conditions. These models often use principles from soil mechanics, rock mechanics, and engineering geology, employing techniques such as limit equilibrium analysis, finite element methods, and numerical modeling to predict slope stability. In contrast, data-driven methods leverage historical data and advanced computational techniques to assess slope stability. These methods include machine learning algorithms, statistical analysis, and pattern recognition, which can identify potential failure mechanisms and predict future instability based on past events and observed patterns. Both approaches have unique advantages and limitations, and the choice between them often depends on the availability of data, the complexity of the slope, and the specific requirements of the project. Researchers have increasingly employed data mining and ensemble methods to develop models to predict slope stability based on environmental and geological factors. [PERSON] and [PERSON] [6] demonstrated the potential of neural networks in accurately predicting slope stability. [PERSON], et al. [7] confirmed the applicability of back propagation neural networks for the Yudonghe landslide. In the machine learning domain, [PERSON] [8] explored support vector machines for prediction based on geotechnical and geometric properties. [PERSON] and [PERSON] [9] compared various AI approaches, emphasizing the importance of advanced algorithms in stability analysis, and [PERSON], et al. [10] and [PERSON], et al. [11] focused on specific failure types, using methods such as the naive Bayes classifier and gradient-boosting machine. [PERSON], et al. [12] introduced a hybrid stacking ensemble method to enhance prediction accuracy by combining finite element analysis with field data. [PERSON], et al. [13] also applied ensemble learning techniques in a case study in Yunyang County, China. [PERSON], et al. [14] introduce an innovative approach combining artificial intelligence (AI) and statistical modeling techniques to enhance earth dam slope stability prediction. Their model significantly improves predictive accuracy, demonstrating its potential as a powerful tool in geo-engineering and offering new avenues for research in complex geo-environmental systems. These studies highlight the growing interest and effectiveness of advanced machine learning and ensemble techniques for accurate, reliable slope stability prediction, underscoring the need for innovative methods to mitigate slope instability risks. Machine learning methods can be categorized into incremental and batch learning based on the need for model retraining with new data [15]. In batch learning models, methods such as Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) are representative.