Rockburst intensity grading prediction based on the LOF-ENN-KNN model

Rockburst is a typical dynamic disaster in deep underground engineering, and its accurate prediction is of great significance to ensure the safety of engineering. Aiming at the key problems in rockburst prediction, such as insufficient analysis of nonlinear correlation characteristics, significant discreteness of small sample data and limited generalization ability of traditional models, this study innovatively constructs a LOF-ENN-KNN coupling prediction model. The model achieves a breakthrough in prediction performance through a three-level progressive processing architecture: noise samples are effectively eliminated by LOF (Local Outlier Factor), category distribution is optimized by ENN (Edited Nearest Neighbour), and high-precision prediction is finally achieved by KNN(K-Nearest Neighbors).In the rockburst database composed of 299 sets of measured data in the world, the LOF-ENN-KNN model shows significant advantages: its overall accuracy rate is 98.93%,which has an overwhelming advantage over the traditional model. Through the comparison and exploration of different sampling methods and different combinations of LOF algorithm, single resampling technology (SMOTE, ADASYN) or simple technology superposition (LOF-SMOTE, LOF-ADASYN) is easy to introduce over-fitting or negative coupling effect, while LOF-ENN-KNN significantly improves the robustness and generalization ability of the model through modular design. In addition, compared with LR, SVM, DT, NBs and DNN intelligent algorithms, LOF-ENN-KNN has significant advantages. Through engineering examples, it is further confirmed that high-precision prediction ability can still be preserved in complex nonlinear parameters, which provides an efficient and reliable technical scheme for rockburst warning in deep engineering.