河北地质大学学报2025，Vol.48Issue(3)：30-37,8.DOI:10.13937/j.cnki.hbdzdxxb.2025.03.005
基于SMOTE算法的岩爆烈度等级预测模型研究
Research on Rockburst Intensity Grade Prediction Model Based on SMOTE Algorithm
摘要
Abstract
In order to solve the problem of data imbalance in the rockburst database,resulting in low prediction accuracy of rockburst,five models were proposed based on the synthetic minority oversampling technique(SMOTE),including SMOTE-random forest,SMOTE-gradient boosting decision tree,SMOTE-support vector machine,SMOTE-BP neural network and SMOTE-convolutional neural network.In this paper,six indicators were selected and the rockburst intensity grade was divided into four grades,so as to establish a rockburst index system.Then,in view of the problem of data imbalance in the rockburst database,the SMOTE oversampling algorithm was used to expand the database.Finally,five commonly used machine learning models were introduced to predict the rockburst intensity level,and these five models were used to predict the original rockburst database and the rockburst database after SMOTE algorithm respectively,to verify the effectiveness of the pretreatment process.The results show that:1)Compared with the traditional model,the prediction accuracy of the model is improved by 10.000%～35.000%after the introduction of SMOTE algorithm;2)Compared with the other four models,the SMOTE-random forest model had the highest prediction accuracy.关键词
岩爆/SMOTE过采样算法/随机森林/烈度等级预测Key words
rockburst/SMOTE oversampling algorithm/random forest/intensity level prediction分类
交通运输引用本文复制引用
李璐佳,周爱红,袁颖,戎密仁..基于SMOTE算法的岩爆烈度等级预测模型研究[J].河北地质大学学报,2025,48(3):30-37,8.基金项目
中央引导地方科技发展资金项目(246Z5405G) （246Z5405G）
