Prediction and Analysis of Slope Stability Based on IPSO-SVM Machine Learning Model

Abstract

In the evaluation and prediction of slope stability, the traditional numerical analysis method, which is over reliant on experience, takes a large amount of computing time and lacks the ability to reflect the fuzzy and nonlinear characteristics of slope parameters well. Considering the above characteristics, this study proposes an improved particle swarm optimization of support vector machine (IPSO-SVM) algorithm model, which combines optimized particle swarm optimization (IPSO) and support vector machine (SVM) and applies it to slope stability prediction. Based on 28 groups of slope engineering data, the stability prediction results of IPSO-SVM, PSO-SVM, and SVM models were compared with real values for analysis. The results show that the maximum relative error of the IPSO-SVM model is only 1.3%, and the average relative error is 1.1%, which is far lower than the prediction error of the PSO-SVM model and SVM model; therefore, the prediction result of IPSO-SVM is the closest to the real value. This method can accurately predict the slope safety factor under the influence of different indexes, and the research results can provide guidance for practical engineering.