[1] 田睿. 基于机器学习的岩爆烈度等级预测模型研究与应用 [D]. 包头: 内蒙古科技大学, 2020. TIAN R. Research and application of rockburst intensity classification prediction model based on machine learning algorithms [D]. Baotou: Inner Mongolia University of Science and Technology, 2020.
[2] 刘剑, 周宗红, 刘军, 等. 基于主成分分析和改进Bayes判别的岩爆等级预测 [J]. 采矿与岩层控制工程学报, 2022, 4(5): 053014. doi: 10.13532/j.jmsce.cn10-1638/td.2022.05.004 LIU J, ZHOU Z H, LIU J, et al. Prediction of rockburst grade based on principal component analysis and improved Bayesian discriminant analysis [J]. Journal of Mining and Strata Control Engineering, 2022, 4(5): 053014. doi: 10.13532/j.jmsce.cn10-1638/td.2022.05.004
[3] 孙飞跃, 刘希亮, 郭佳奇, 等. 岩爆预测评估方法的动力数值分析 [J]. 应用力学学报, 2022, 39(1): 26–34. doi: 10.11776/j.issn.1000-4939.2022.01.004 SUN F Y, LIU X L, GUO J Q, et al. Dynamic numerical calculation analysis of rockburst prediction assessment methods [J]. Chinese Journal of Applied Mechanics, 2022, 39(1): 26–34. doi: 10.11776/j.issn.1000-4939.2022.01.004
[4] 曲宏略, 刘哲言, 杨龙, 等. 基于应力判据的隧道岩爆预测评估研究 [J]. 地下空间与工程学报, 2020, 16(Suppl 2): 934–938,956. QU H L, LIU Z Y, YANG L, et al. Prediction and evaluation of rock burst in tunnel based on stress criterion [J]. Chinese Journal of Underground Space and Engineering, 2020, 16(Suppl 2): 934–938,956.
[5] DOU L M, CAI W, CAO A Y, et al. Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices [J]. International Journal of Mining Science and Technology, 2018, 28(5): 767–774. doi: 10.1016/j.ijmst.2018.08.007
[6] CAO A Y, JING G C, DING Y L, et al. Mining-induced static and dynamic loading rate effect on rock damage and acoustic emission characteristic under uniaxial compression [J]. Safety Science, 2019, 116: 86–96. doi: 10.1016/j.ssci.2019.03.003
[7] 温廷新, 陈依琳. 基于海林格距离和AHDPSO-ELM的岩爆烈度等级预测模型 [J]. 中国安全科学学报, 2022, 32(11): 38–46. doi: 10.16265/j.cnki.issn1003-3033.2022.11.1915 WEN T X, CHEN Y L. Prediction model of rockburst intensity grade based on Hellinger distance and AHDPSO-ELM [J]. China Safety Science Journal, 2022, 32(11): 38–46. doi: 10.16265/j.cnki.issn1003-3033.2022.11.1915
[8] 李明亮, 李克钢, 秦庆词, 等. 岩爆烈度等级预测的机器学习算法模型探讨及选择 [J]. 岩石力学与工程学报, 2021, 40(Suppl 1): 2806−2816. LI M L, LI K G, QIN Q C, et al. Discussion and selection of machine learning algorithm model for rockburst intensity grade prediction [J]. Chinese Journal of Rock Mechanics and Engineering, 201, 40(Suppl 1): 2806−2816.
[9] 侯克鹏, 包广拓, 孙华芬. 改进的MVO-GRNN神经网络岩爆预测模型研究 [J]. 安全与环境学报, 2024, 24(3): 923–932. doi: 10.13637/j.issn.1009-6094.2023.0341 HOU K P, BAO G T, SUN H F. Research on improved MVO-GRNN neural network rockburst prediction model [J]. Journal of Safety and Environment, 2024, 24(3): 923–932. doi: 10.13637/j.issn.1009-6094.2023.0341
[10] 满轲, 武立文, 刘晓丽, 等. 基于灰色关联分析和SSA-RF模型的岩爆等级预测 [J]. 金属矿山, 2023(5): 202–212. doi: 10.19614/j.cnki.jsks.202305021 MAN K, WU L W, LIU X L, et al. Rockburst grade prediction based on grey correlation analysis and SSA-RF model [J]. Metal Mine, 2023(5): 202–212. doi: 10.19614/j.cnki.jsks.202305021
[11] 高梅, 张成良, 张华超, 等. 基于SMOTEENN-CGAN-Stacking的岩爆烈度等级预测研究 [J]. 工程地质学报, 2024, 32(6): 2264–2276. doi: 10.13544/j.cnki.jeg.2024-0112 GAO M, ZHANG C L, ZHANG H C, et al. Rockburst intensity level prediction based on SMOTEENN-CGAN-Stakcing [J]. Journal of Engineering Geology, 2024, 32(6): 2264–2276. doi: 10.13544/j.cnki.jeg.2024-0112
[12] 苏焕博. 数据缺失和不均衡下的IDPSO-ELM岩爆烈度等级预测模型研究 [D]. 阜新: 辽宁工程技术大学, 2022. SU H B. Research on IDPSO-ELM rockburst intensity grade prediction model with missing and unbalanced data [D]. Fuxin: Liaoning Technical University, 2022.
[13] ZHOU J, LI X, MITRI H S. Classification of rockburst in underground projects: comparison of ten supervised learning methods [J]. Journal of Computing in Civil Engineering, 2016, 30(5): 04016003. doi: 10.1061/(ASCE)CP.1943-5487.0000553
[14] 武立文. 基于SSA-RF模型的岩爆预测方法及应用研究 [D]. 北京: 北方工业大学, 2024. WU L W. Research on rockburst prediction method and application based on SSA-RF model [D]. Beijing: North China University of Technology, 2024.
[15] 刘晓悦, 杨伟, 张雪梅. 基于改进层次法与CRITIC法的多维云模型岩爆预测 [J]. 湖南大学学报(自然科学版), 2021, 48(2): 118–124. doi: 10.16339/j.cnki.hdxbzkb.2021.02.015 LIU X Y, YANG W, ZHANG X M. Rockburst prediction of multi-dimensional cloud model based on improved hierarchical analytic method and critic method [J]. Journal of Hunan University (Natural Sciences), 2021, 48(2): 118–124. doi: 10.16339/j.cnki.hdxbzkb.2021.02.015
[16] 谭文侃, 叶义成, 胡南燕, 等. LOF与改进SMOTE算法组合的强烈岩爆预测 [J]. 岩石力学与工程学报, 2021, 40(6): 1186–1194. doi: 10.13722/j.cnki.jrme.2020.1035 TAN W K, YE Y C, HU N Y, et al. Severe rock burst prediction based on the combination of LOF and improved SMOTE algorithm [J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 40(6): 1186–1194. doi: 10.13722/j.cnki.jrme.2020.1035
[17] 王宇航, 周宗红, 李国才, 等. 基于数据预处理的岩爆等级预测模型及精度优化 [J]. 矿业研究与开发, 2024, 44(11): 101–109. doi: 10.13827/j.cnki.kyyk.2024.11.009 WANG Y H, ZHOU Z H, LI G C, et al. Prediction model and accuracy optimization of rockburst grade based on data preprocessing [J]. Mining Research and Development, 2024, 44(11): 101–109. doi: 10.13827/j.cnki.kyyk.2024.11.009
[18] 翁嘉诚, 周晓杰, 叶蓓蕾, 等. 基于改进麻雀搜索算法的K-means聚类 [J]. 数学的实践与认识, 2024, 54(2): 152–166. WENG J C, ZHOU X J, YE B L, et al. K-means clustering based on improved sparrow search [J]. Mathematics in Practice and Theory, 2024, 54(2): 152–166.
[19] 杜云, 周志奇, 贾科进, 等. 混合多项自适应权重的混沌麻雀搜索算法 [J]. 计算机工程与应用, 2024, 60(7): 70–83. doi: 10.3778/j.issn.1002-8331.2307-0254 DU Y, ZHOU Z Q, JIA K J, et al. Chaotic sparrow search algorithm with mixed multinomial adaptive weights [J]. Computer Engineering and Applications, 2024, 60(7): 70–83. doi: 10.3778/j.issn.1002-8331.2307-0254
[20] 毛清华, 张强. 融合柯西变异和反向学习的改进麻雀算法 [J]. 计算机科学与探索, 2021, 15(6): 1155–1164. doi: 10.3778/j.issn.1673-9418.2010032 MAO Q H, ZHANG Q. Improved sparrow algorithm combining cauchy mutation and opposition-based learning [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1155–1164. doi: 10.3778/j.issn.1673-9418.2010032
[21] 张建涛, 刘志祥, 张双侠, 等. 基于WOA-RF的边坡稳定性预测模型 [J]. 高压物理学报, 2024, 38(3): 035301. doi: 10.11858/gywlxb.20230837 ZHANG J T, LIU Z X, ZHANG S X, et al. Slope stability prediction based on WOA-RF hybrid model [J]. Chinese Journal of High Pressure Physics, 2024, 38(3): 035301. doi: 10.11858/gywlxb.20230837