Research on Slope Stability Analysis Based on Machine Learning

As a hot artificial intelligence method, machine learning has penetrated into various industries. It has also been gradually introduced into the field of slope prevention and control, which has greatly promoted the development of this field. Therefore, this paper takes machine learning as the main research method, and uses the advantages of machine learning algorithm in regression and clustering to explore its application in slope stability analysis. This paper firstly analyzes the machine learning algorithm and its modeling process. Then the XGBoost algorithm is used for modeling and the genetic algorithm is used for model optimization. Based on the optimized model to evaluate the slope stability of Ningnan County, the results show that the optimized XGBoost evaluation model has the best effect, the evaluation accuracy rate is 89%, which is 1%~9% higher than other models, and the AUC value is 0.87, which is high. 0.05~0.13 for other models. And the time required is 29.26 seconds shorter than grid search, and the optimization efficiency is greatly improved.