基于SMOTE过采样和GBDT框架下集成算法的岩爆风险评估

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中文摘要：岩爆是地下工程常见的一种地质灾害，严重威胁人员、设备和财产安全。利用机器学习模型评估岩爆风险正逐渐成为一种趋势，这对地质灾害预防具有重要意义。本文采用梯度提升决策树(GBDT)框架下的集成算法对岩爆倾向性开展评估和分类。首先，从案例数据库中获得301个岩爆数据样本，采用合成少数类过采样技术(SMOTE)对数据进行预处理;然后，建立包括GBDT、极限梯度提升算法(XGBoost)、轻量梯度提升算法(LightGBM)和类别特征梯度提升算法(CatBoost)的岩爆评价模型，并通过随机搜索网格及五折交叉验证获取模型的最优超参数;之后，使用最优超参数配置，拟合评估模型，并利用测试集对模型进行分析。为了评估模型性能，选取准确率、精度、召回率和F1-score等进行分析，并与其他机器学习模型进行比较分析。最后，利用训练好的模型，对位于陕西省某矿山不同的岩石试样进行了岩爆风险评估，为矿山的安全生产工作提供了理论指导。结果表明，GBDT框架下的算法模型在岩爆水平评估中表现良好，所提出的方法可为岩爆风险等级分析和安全管理提供可靠参考。

外文标题：Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

外文摘要：Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

外文关键词：

rockburst evaluationSMOTE oversamplingrandom search gridK-fold cross-validationconfusion matrix

作者：

王加闯、董陇军

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作者单位：

School of Resources and Safety Engineering, Central South University, Changsha 410083, China

关键词：

岩爆评价 SMOTE过采样随机搜索网格算法 K-fold交叉验证混淆矩阵

基金：

International Cooperation and Exchange of the National Natural Science Foundation of ChinaChina Scholarship Council

项目编号：

52161135301202306370296

出版年：

2024

DOI：

10.1007/s11771-024-5782-5

中南大学学报(英文版)

中南大学

中南大学学报(英文版)

CSTPCDEI

影响因子：0.47

ISSN：2095-2899

年,卷(期)：2024.31(8)