Prediction of Rockburst Grade Based on BKA-CNN-SVM Model

In order to realize efficient and accurate rockburst grade prediction, and prevent underground engineering disasters, this paper proposes a prediction model based on black-winged kite optimization algorithm-convolutional neural network-support vector machine (BKA-CNN-SVM). Firstly, the prediction index system was established according to six influence factors of rockburst, and 284 groups of rockburst cases at home and abroad were collected to establish a rockburst database. Secondly, Laida criterion and 1.5 times quartile difference were introduced to remove and replace the outliers in the data. The kernel principal component analysis (KPCA) was used to reduce the dimension of the data and extract the features. The extracted features were used as the model inputs. Finally, the confusion matrix was used to evaluate the model performance in terms of accuracy, precision, recall, and F₁ value. BKA-CNN-SVM model was compared with convolutional neural network (CNN) model, extreme learning machine (ELM) model, and convolutional neural network and support vector machine (CNN-SVM) integrated model. The results showed that the accuracy, precision, F₁ value, and recall of BKA-CNN-SVM model are 95.35%, 0.89, 0.92, and 0.94, respectively, which are significantly better than the other models in terms of prediction accuracy and generalization degree. In order to verify the feasibility of the BKA-CNN-SVM model, it was used to prediction the rockburst grade of the Jinping secondary hydro-power station. The prediction results have high consistency with the actual field conditions. This research can provides a new method for rockburst grade prediction.