# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 1 Introduction
The regression prediction model combined with a neural network, a deep learning model, and various algorithms further improves the accuracy, but the complexity of the model also increases, making the model more difficult to explain. [PERSON] et al. [14] combined SHAP and XGBoost to explain landslide susceptibility assessment and found that peak rainfall intensity and altitude were the dominant factors affecting landslide occurrence. [PERSON] et al. [15] proposed a PSO-SVM coupled model to predict landslide displacement and verified the prediction results by taking a typical step landslide in the Three Gorges Reservoir area as an example. Their results show that rainfall and water level are the dominant factors of landslide deformation. [PERSON] et al. [16] used the variational mode theory to decompose landslide step displacement, determined the mathematical relationship between random displacement and trigger factors through the GMO-BP model, and obtained the cumulative displacement prediction based on the time series model, which provided a certain reference for landslide displacement warning. In addition, some scholars have integrated the theory of rock mass mechanics to build a mixed model of rock mass mechanics and machine learning. [PERSON] et al. [17] built a multi-index system based on the Hawke Brown criterion, combined the sparrow search algorithm with a multilevel perceptual neural network, and verified the effectiveness of the model through practical cases. The results can be applied to most rock slopes. Existing machine learning models, including Random Forest (RF), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM), have shown promise in slope stability prediction but are subject to significant limitations. Specifically, RF is prone to overfitting when applied to high-dimensional datasets with limited samples, thereby diminishing its generalization capabilities in complex geological settings [8]. LSTM networks, designed primarily for sequential data and temporal dependencies, are not well-suited for static slope stability datasets. This mismatch leads to suboptimal accuracy and unnecessary computational overhead [18]. SVM faces challenges with large-scale datasets due to its reliance on quadratic programming, and its performance is highly sensitive to the choice of kernel function, introducing potential bias [19]. To address the limitations of traditional machine learning methods in data processing, feature selection, and interpretability, this study proposes a novel approach that integrates the comprehensive index of the M-C strength criterion with slope stability influencing factors. This integration effectively mitigates the complexity associated with feature selection. Furthermore, we employ BKA, a new metaheuristic intelligent algorithm, to optimize the primary structural parameters of the multilevel perceptron neural network, thereby enhancing the robustness and generalization capabilities of the model. The optimal model is interpreted using the SHAP method, revealing its physical significance in predictions and making the model's decision-making process more transparent. The findings of this research provide valuable guidance for the development of slope stability control measures and hold significant practical engineering implications.