# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 1 Introduction  
The issue of slope stability is not only pertinent to mining, geology, water conservancy, and other engineering disciplines but is also closely linked to environmental protection and urban planning. When an unstable slope experiences dynamic disturbances such as excavation, blasting, or earthquakes, it may result in catastrophic events like collapses and landslides, leading to casualties and significant property damage [1, 2]. With the intensification of global climate change and the ongoing expansion of human activities, the frequency and scale of slope instability incidents are on the rise. This trend underscores the urgent need for accurate and efficient assessments of slope stability [3]. Traditional methods for analyzing slope stability--such as limit equilibrium analysis and finite element modeling--while providing valuable insights under certain conditions, often face limitations due to complex geological settings, nonlinear material behaviors, and external factors that are challenging to quantify (e.g., rainfall or seismic activity). These constraints can introduce uncertainties in practical applications. In recent years, machine learning has emerged as a powerful data-driven modeling tool that offers distinctadvantages in addressing complex systems with high-dimensional data. This development paves new avenues for predicting slope stability effectively [4; 5]. [PERSON] et al. [6] built an SSA-SVM slope instability intelligent prediction model on the basis of establishing a database composed of 304 slope cases. Their results show that the SSA-SVM model performs well in various indicators and has certain competitiveness in slope classification prediction. [PERSON] et al. [7] put forward a method to predict slope stability by using the Bayesian discriminant analysis theory. The model has good prediction performance and has certain practical engineering significance. [PERSON] et al. [8] proposed a hybrid model of random forest optimized by the whale algorithm. Compared with the results of other models, it was found that all indexes of WOA-RF performed well, and the bulk density was found to be the most sensitive feature affecting slope stability. [PERSON] et al. [9] combined the metaheuristic algorithm with machine learning, optimized LS-SVC with the firefly algorithm, and built a comprehensive prediction model. Experimental results showed that the classification accuracy of the new hybrid AI model was improved by about 4%. The above scholars used different algorithms and machine learning methods to build slope stability prediction models, which provided certain positive effects for slope stability assessment, but there were certain limitations in slope stability identification. Instability and stability represent only two states of slope conditions, which fail to fully capture the complexity and dynamic nature of slope behavior [10]. Therefore, employing a regression model offers distinct advantages in predicting slope stability. The most widely utilized metric is the safety factor, as its value can precisely indicate the overall condition of the slope. [PERSON] et al. [11] proposed a Stacking integrated learning model (BP-Stacking) based on Bayesian optimization. Their results found that the BOPStacking method can effectively improve the accuracy of predicting slope safety factors, and the mean square error is lower than that of a single model, and complex calculations are avoided. [PERSON] et al. [12] used the sparrow search method to optimize the weight and bias parameters of the BP neural network, built an optimal model and tested it, and found that the predicted value of the optimized model was closer to the actual value. [PERSON] [13] used the combination of the GM model and RBF neural network to build a GM-RBF combined deformation prediction model for high cutting slope, and the results showed that the combined model was more feasible than the single model.