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[1]邵良杉,高英超.基于MCMC填补的SSA-SVM煤与瓦斯突出预测模型*[J].中国安全生产科学技术,2023,19(8):94-99.[doi:10.11731/j.issn.1673-193x.2023.08.014]
　SHAO Liangshan,GAO Yingchao.SSA-SVM prediction model of coal and gas outburst based on MCMC filling[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(8):94-99.[doi:10.11731/j.issn.1673-193x.2023.08.014]
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基于MCMC填补的SSA-SVM煤与瓦斯突出预测模型*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
19
期数:
2023年8期
页码:
94-99
栏目:
职业安全卫生管理与技术
出版日期:
2023-08-31

文章信息/Info

Title:
SSA-SVM prediction model of coal and gas outburst based on MCMC filling
文章编号:
1673-193X(2023)-08-0094-06
作者:
邵良杉1; 2; 高英超1
（1.辽宁工程技术大学 系统工程研究所，辽宁 葫芦岛 125105；
2.辽宁理工学院，辽宁 锦州 121000）
Author(s):
SHAO Liangshan1; 2;  GAO Yingchao1
（1.Institute of Systems Engineering,Liaoning Technical University,Huludao Liaoning 125105,China；
2.Liaoning Institute of Science and Engineering,Jinzhou Liaoning 121000,China）
关键词:
煤与瓦斯突出预测; 马尔科夫链蒙特卡罗（MCMC）; 麻雀搜索算法（SSA）; 数据填补; 支持向量机（SVM）
Keywords:
coal and gas outburst prediction;  Markov chain Monte Carlo (MCMC);  sparrow search algorithm (SSA);  data filling;  support vector machines (SVM)
分类号:
TD713；X936
DOI:
10.11731/j.issn.1673-193x.2023.08.014
文献标志码:
A
摘要:
为提升煤与瓦斯突出预测准确度，减小数据缺失对煤与瓦斯突出预测的不利影响，提出1种基于链式多重填补马尔科夫链蒙特卡罗（MCMC）的麻雀搜索算法（SSA）优化支持向量机（SVM）预测模型。根据突出影响因素选取模型参数，运用MCMC对突出事故缺失值进行数据填补，采用SSA优化SVM，建立MCMC-SSA-SVM模型对填补后数据集进行预测，验证MCMC填补有效性和SSA优化性能；分别构建SVM、SSA-SVM、PSO-SVM、GAM-SVM、CMC-SVM、MCMC-PSO-SVM和MCMC-GA-SVM这7种模型进行突出预测，对比预测准确度，分析MCMC-SSA-SVM、MCMC-PSO-SVM和MCMC-GA-SVM的适应度。研究结果表明：MCMC填补后准确度均提升7.89个百分点以上，SSA的优化性能强于PSO和GA，MCMC-SSA-SVM预测准确度最高，为97.37%，泛化能力优于对比模型。研究结果可为煤与瓦斯突出预测研究提供借鉴和参考。
Abstract:
To improve the accuracy of coal and gas outburst prediction and reduce the adverse effect of missing data on coal and gas outburst prediction,a sparrow search algorithm (SSA) optimized support vector machine (SVM) prediction model based on chain multiple filling Markov chain Monte Carlo (MCMC) was proposed.The model parameters were selected according to the influencing factors of outburst,and the MCMC algorithm was applied to fill in the missing values of outburst accidents,then SSA was used to optimize SVM.A MCMC-SSA-SVM model was established to predict the filled data set,and the effectiveness of MCMC filling and the optimization performance of SSA were verified.Seven models,namely SVM,SSA-SVM,PSO-SVM,GA-SVM,MCMC-SVM,MCMC-PSO-SVM and MCMC-GA-SVM,were constructed respectively for outburst prediction to compare the accuracy,and the adaptability of MCMC-SSA-SVM,MCMC-PSO-SVM and MCMC-GA-SVM were analyzed.The results show that all the accuracies of MCMC after filling increase by more than 7.89 percentage points,the optimization performance of SSA is stronger than those of PSO and GA,MCMC-SSA-SVM has the highest prediction accuracy of 97.37%,and the generalization ability is better than the comparison models.The results can provide reference for the research on coal and gas outburst prediction.

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备注/Memo

备注/Memo:
收稿日期： 2023-03-06
* 基金项目： 国家自然科学基金项目（71771111）
作者简介： 邵良杉，博士，教授，主要研究方向为矿业系统工程等。
通信作者： 高英超，硕士研究生，主要研究方向为矿业系统工程。
更新日期/Last Update: 2023-09-07