Comparative Analysis of Various Machine Learning Techniques for Rockburst Risk Prediction

This paper investigates the application of machine learning algorithms in rockburst risk prediction models. It begins with an overview of fundamental machine learning principles, followed by a detailed analysis of common classification algorithms, including Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and AdaBoost. Subsequently, the paper examines the use of clustering algorithms for feature extraction and reorganization. The technical process for developing accurate rockburst risk prediction models is outlined, encompassing data preprocessing, feature clustering, reorganization, and model evaluation. The findings offer valuable insights and references for the integration of machine learning in rockburst risk prediction, as well as innovative approaches for research and practical applications in related fields.