Prediction of Rockburst Intensity Grade Based on SVM and Adaptive Boosting Algorithm

Accurate prediction of rockburst intensity grade is of great significance for mitigating and eliminating rockburst hazards. Aiming at the problems of uncertain feature selection and low prediction accuracy of rockburst intensity grade prediction model, in this paper it proposes a rockburst grade prediction model based on SSA-SVM-AdaBoost algorithm with ReliefF-Pearson feature selection. The method combines the weight idea of ReliefF and the correlation principle of Pearson coefficient to select feature indexes, and SSA-SVM-AdaBoost algorithm is proposed by using the sparrow search algorithm (SSA) optimized support vector machine(SVM)classifier as the AdaBoost weak classifier to solve the multiclassification problem. First, 7 kinds of feature indicators are selected to form the original feature space by analyzing rockburst case data, then the 4⁃dimension advantage features are selected by ReliefF-Pearson method. The data is processed with random oversampling before input SSA-SVM-AdaBoost prediction model. The research results show that the feature selection method based on ReliefF-Pearson can effectively extract advantage feature indicators. Compared with SSA-SVM and AdaBoost based on single-layer decision tree, the prediction accuracy of SSA-SVM-AdaBoost model is improved by 12.5%, and 31.25% compared with SVM. It shows that SSA-SVM as a weak classifier is better than a single-layer decision tree in classification performance, and the AdaBoost enhancement algorithm integrating multiple single classifiers is better than a single classification model. Data oversampling process does not affect the accuracy of the model prediction set, but improves the prediction accuracy of the training set. It is proved that the proposed model can be effectively applied to rockburst intensity grade prediction, which provides a new perspective for this problem.