To address issues related to excessive human influence and prolonged prediction times in rockburst prediction,we propose a rockburst intensity classification prediction model based on automatic machine learning.This model is trained using five automatic machine learning frameworks and evaluated using metrics such as accuracy,precision,recall,and Fl-score.Subsequently,we compare the performance of this trained model with results from thirteen common machine learning models.The model developed with the Auto-Sklearn framework achieved a high accuracy of 0.969,while the model created with the Auto-Gluon framework,although having the lowest accuracy among the five frameworks,still achieved an accuracy of 0.927.Rockburst prediction models constructed using AutoML frameworks significantly outperformed traditional machine learning algorithms.The Auto-Sklearn-based model exhibited the highest accuracy.In summary,the optimized model was applied to predict rockburst events at the Shaiqi River phosphate mine,and the predictions were consistent with the actual observations on-site.This indicates that the automatic machine learning-based model for rockburst intensity classification prediction can accurately predict rockburst incidents in real-world engineering settings.
维普
万方数据