Slope stability prediction method based on the margin distance minimization selective ensemble

• Evaluate the slope stability through the characteristic parameters objectively. • Combining homogeneous ensemble and heterogeneous ensemble method. • Slope stability prediction model based on selective ensemble technology. • The prediction effect is better than common machine learning models. • Overcome the difficulty of model selection and the risk of misjudgment. In order to evaluate the slope stability quickly, accurately, and reliably, a slope stability prediction method based on the margin distance minimization selective ensemble (MDMSE) is proposed, which can objectively evaluate the slope stability through the basic geometric and geological factors, overcoming the disadvantages of difficult selection and high risk of misjudgment in traditional machine learning models. Firstly, a large number of differentiated individual learners are built by the method of data sample and algorithm parameter perturbation. Then, based on the MDMSE algorithm, search the optimal subset of individual learners. Finally, integrate them to form a reasonable and effective MDMSE prediction model with the majority voting method. With 422 groups of slope samples, and the prediction performance of the MDMSE prediction model is compared with the common single machine learning models and ensemble models. The results show that the MDMSE prediction model is of obviously better generalization ability than other models, and better recognition accuracy and faster identification speed than all ensemble models. As a selective ensemble model with strong generalization ability and high efficiency. The MDMSE prediction model is more suitable for the prediction and analysis of slope stability and has certain engineering references and practical value.