| 煤矿深部开采冲击地压监测解危关键技术研究 |
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| 引用本文： | 吴丙梅, 薛生, 杨超宇. 基于集成学习方法的冲击地压危险性预测研究[J]. 矿业安全与环保, 2023, 50(1): 54-59. DOI: 10.19835/j.issn.1008-4495.2023.01.009 |
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| 作者姓名： | 吴丙梅 薛生 杨超宇 |
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| 作者单位： | 1.安徽理工大学 安全科学与工程学院，安徽 淮南 232001;2.安徽理工大学 煤炭安全开采国家地方联合工程研究中心，安徽 淮南 232001;3.安徽理工大学 经济与管理学院，安徽 淮南 232001 |
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| 基金项目： | 国家自然科学基金项目(51934007) |
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| 摘 要： | 为了进一步提高冲击地压危险性预测的准确性，利用集成学习方法对冲击地压发生的主要因素指标进行了分析，分别采用集成学习方法中7种分类预测模型对冲击地压危险性进行了预测，实验结果表明, 7种模型均具有一定的可靠性，将模型的准确度和海明损失作为评价指标，得出XGBoost算法具有较高的预测性能，可以相对有效地对冲击地压的危险性进行预测。最后，利用SHAP值对XGBoost模型进一步解释，冲击地压危险性受弹性能指数的影响最大。
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| 关 键 词： | 冲击地压 危险性预测 集成学习 海明损失 SHAP值 XGBoost |
| 收稿时间： | 2021-11-26 |
| 修稿时间： | 2022-01-23 |
Classification of rockburst in underground projects: Comparison of ten supervised learning methods |
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| WU Bingmei, XUE Sheng, YANG Chaoyu. Research on prediction of rock burst risk based on ensemble learning method[J]. Mining Safety & Environmental Protection, 2023, 50(1): 54-59. DOI: 10.19835/j.issn.1008-4495.2023.01.009 |
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| Authors: | WU Bingmei XUE Sheng YANG Chaoyu |
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| Affiliation: | 1.School of Safety Science and Engineer, Anhui University of Science and Technology, Huainan 232001, China;2.Joint National-Local Engineering Research Centre of Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China;3.School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China |
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| Abstract: | In order to further improve the prediction accuracy of the rock burst risk, the ensemble learning (EL) method was used to analyze the main factors and indicators of the occurrence of rock burst. Seven kinds of classification prediction models in the EL method were respectively used to predict the rock burst risk. The experimental results show that all the seven models have certain reliability. Taking the accuracy and Haiming loss of the models as evaluation indexes, it is concluded that the XGBoost algorithm has high prediction performance and can predict the rock burst risk relatively effectively. Finally, the SHAP value is used to further explain the XGBoost model. The elastic energy index has the greatest influence on the rock burst risk. |
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| Keywords: | rock burst risk prediction ensemble learning Hamming loss SHAP value XGBoost |
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