Prediction Model and Accuracy Optimization of Rockburst Grade Based on Data Preprocessing
The accuracy of rockburst prediction has an important practical significance for the prediction of rock mass engineering disasters.Accurate and effective data preprocessing is the basis of subsequent prediction work.The rockburst database was established by collecting 471 groups of rockburst cases at home and abroad.The maximum tangential stress,compressive strength,tensile strength and elastic energy indexes of surrounding rocks were selected as the characteristic indexes,and the prediction model was constructed by combining 10 machine learning algorithms.In order to eliminate the interference of outliers in the samples to the prediction model,the outlier cleaning range was reduced to a single level,and the outliers were detected and processed step by step according to the rockburst intensity level.An adaptive oversampling(ADASYN)was proposed to improve the data distribution,and the sample synthesis of the original minority class data was carried out under the condition of retaining the characteristics of the minority class sample data,so as to solve the problem of sample imbalance of each rock burst grade.The genetic algorithm(GA)was introduced to optimize the parameters of the high stability model,and the model was deeply evaluated by combining the confusion matrix and multiple evaluation indexes.The research shows that the ADASYN method improves the comprehensive accuracy of the model by 11.58％,and GA-XGBoost model has been selected as the optimal performance.The prediction accuracy and weighted average F1 value reach 93％.The model was applied to the JinpingⅡ Hydropower Station,Sanshandao Gold Mine,and Maluping Mine,and the predicted results showed good consistency with the on-site conditions,providing a new method for predicting rock bursts in the future.
万方数据
维普
