Classification and Prediction of Slope Stability of Open-Pit Mine with Support Vector Machine Based on Chaotic Particle Swarm Optimization
In order to simply and effectively evaluate slope stability,four machine learning models based on chaotic particle swarm optimization(CPSO)were proposed to solve the existing problems of algorithm selection and hyper-parameter optimization in traditional machine learning model,and their prediction performance were comprehensively compared among each other.A database consisting of 221 open-pit slope stability cases was established,in which 80%of the data were used for training and 20%for model testing.Based on the comparison between the prediction results of four models and the verification results of engineering practices,it is found that the support vector machine(SVM)based on CPSO is superior than other three machine learning models in terms of prediction of slope stability,presenting an accuracy up to 88%.Thus,it can provide a reliable prediction for the safety of slope in open-pit mine.
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