Rock Burst Prediction Based on Data Preprocessing and Improved Sparrow Algorithm

To solve the problems of outlier samples, imbalanced samples, and local optimal of sparrow search algorithm in machine learning rockburst prediction, this paper established a rockburst prediction model from two perspectives of data preprocessing and algorithm improvement. First, based on lithology conditions and stress conditions, selected the maximum tangential stress, compressive strength, tensile strength and elastic energy index of surrounding rock as the characteristic indexes, and used three kinds of machine learning algorithms combined with 5-fold cross-validation method to construct the prediction model. In the data pre-processing stage, collected 174 groups of domestic and international rock burst cases to establish a database; for outlier samples, introduced the local outlier factor (LOF) algorithm to detect and eliminate outlier samples step by step according to the rock burst class; for sample imbalance, the adaptive synthetic sampling method (ADASYN) was introduced to increase the number of minority class samples. Three hybrid strategies were employed to improve sparrow search algorithm (ISSA) was used to optimize the parameters of three machine learning algorithms, namely limit gradient lift tree (XGBoost), random forest (RF) and multi-layer perceptron (MLP). Multiple evaluation indexes such as accuracy rate and precision rate were analyzed and discussed to verify the effectiveness of the model. The results show that the accuracy of the newly constructed optimal model, ISA-XGBoost, reaches 94.12%, indicating high prediction accuracy. In addition to the feature importance analysis of the four feature indexes, it was determined that the maximum tangential stress of the surrounding rock is the most important feature.