|本期目录/Table of Contents|

[1]温廷新,苏焕博.基于链式多重插补的WOA-ELM煤与瓦斯突出预测模型*[J].中国安全生产科学技术,2022,18(7):68-74.[doi:10.11731/j.issn.1673-193x.2022.07.010]
　WEN Tingxin,SU Huanbo.WOA-ELM prediction model of coal and gas outburst based on multiple imputation by chained equations[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(7):68-74.[doi:10.11731/j.issn.1673-193x.2022.07.010]
点击复制

基于链式多重插补的WOA-ELM煤与瓦斯突出预测模型*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年7期
页码:
68-74
栏目:
职业安全卫生管理与技术
出版日期:
2022-07-31

文章信息/Info

Title:
WOA-ELM prediction model of coal and gas outburst based on multiple imputation by chained equations
文章编号:
1673-193X(2022)-07-0068-07
作者:
温廷新; 苏焕博
（辽宁工程技术大学 工商管理学院，辽宁 葫芦岛 125105）
Author(s):
WEN Tingxin;  SU Huanbo
(School of Business Administration,Liaoning Technical University,Huludao Liaoning 125105,China)
关键词:
煤与瓦斯突出预测; 缺失数据; 链式支持向量机多重插补（MICE_SVM）方法; 鲸鱼优化算法（WOA）; 极限学习机（ELM）
Keywords:
coal and gas outburst prediction;  missing data;  multiple imputation by chained support vector machine (MICE_SVM) method;  whale optimization algorithm (WOA);  extreme learning machine (ELM)
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.07.010
文献标志码:
A
摘要:
为了提高缺失数据下煤与瓦斯突出预测准确率，提出1种基于链式支持向量机多重插补（MICE_SVM）的鲸鱼优化算法（WOA）-极限学习机（ELM）预测模型，以淮南朱集矿区为例，选取5个煤与瓦斯突出影响指标作为模型特征，采用提出的MICE_SVM算法插补突出事故数据中缺失值，利用WOA优选ELM输入层权值及隐含层阈值，构建煤与瓦斯突出预测模型，将插补后数据用于WOA-ELM模型的训练与测试，并与其他模型的预测效果对比。研究结果表明：MICE_SVM插补前、后的有突出数据预测准确率分别为83.02%，90.41%，MICE_SVM显著提高了有突出预测准确率，对无突出和整体的预测准确率提高不明显；数据插补后WOA优化ELM对无突出、有突出和整体的预测准确率分别为97.94%，96.25%，96.48%，较优化前分别提高了5.79%，5.84%，5.55%，数据插补后WOA-ELM为最佳预测模型。
Abstract:
In order to improve the prediction accuracy of coal and gas outburst under missing data,a whale optimization algorithm (WOA)-extreme learning machine (ELM) prediction model based on the chained support vector machine (MICE_SVM) was proposed.Taking Zhuji mining area in Huainan as an example,five indicators influencing coal and gas outburst were selected as the model characteristics,then the proposed MICE_SVM algorithm was used to interpolate the missing values of the outburst data,and the WOA was used to optimize the input layer weights and hidden layer thresholds of ELM.A prediction model of coal and gas outburst was constructed,then the interpolated data were used to train and test the WOA-ELM model,and the prediction effect was compared with other models.The results showed that the prediction accuracy of outburst data before and after MICE_SVM interpolation was 83.02% and 90.41%,respectively,and MICE_SVM significantly improved the prediction accuracy of outburst data,but the increase in the non-outburst data and whole prediction accuracy was not obvious.After the data interpolation,the prediction accuracy of WOA optimized ELM for non-outburst,outburst and whole was 97.94%,96.25% and 96.48%,respectively,which increased by 5.79%,5.84% and 5.55% than that before optimization,so the WOA-ELM after data interpolation was the best prediction model.

参考文献/References:

［1］梁跃强,林辰,宫伟东,等.投影寻踪聚类方法在煤与瓦斯突出危险性预测中的应用［J］.中国安全生产科学技术，2017，13(1):46-50. LIANG Yueqiang,LIN Chen,GONG Weidong,et al.Application of projection pursuit clustering method in coal and gas outburst risk prediction ［J］.Journal of Safety Science and Technology，2017，13(1)：46-50.
［2］LI B,WANG E Y,SHANG Zh,et al.Optimize the early warning time of coal and gas outburst by multi-source information fusion method during the tunneling process［J］.Process Safety and Environmental Protection，2021，149:839-849.
［3］毕慧杰,任延平,张浩浩,等.基于多因素模式识别的煤与瓦斯突出预测研究［J］.中国安全生产科学技术,2017，13(6):98-103. BI Huijie,REN Yanping,ZHANG Haohao,et al.Research on coal and gas outburst prediction based on multi factor pattern recognition［J］.Journal of Safety Science and Technology，2017,13(6):98-103.
［4］郑晓亮,来文豪,薛生.MI和SVM算法在煤与瓦斯突出预测中的应用［J］.中国安全科学学报,2021,31(1):75-80. ZHENG Xiaoliang,LAI Wenhao,XUE Sheng.Application of MI and SVM in coal and gas outburst prediction［J］.China Safety Science Journal,2021,31(1):75-80.
［5］RU Y D,LYU X F,GUO J K,et al.Real-Time prediction model of coal and gas outburst［J］.Mathematical Problems in Engineering,2020.https://doi.org/10.1155/2020/2432806.
［6］HE M.Agas outburst prediction model based on data mining and information fusion ［J］.Rev.d’Intelligence Artif.,2019,33(5):379-386.
［7］韩永亮,李胜,胡海永,等.基于改进的GA-ELM煤与瓦斯突出预测模型［J］.地下空间与工程学报,2019,15(6):1895 -1902. HAN Yongliang,LI Sheng,HU Haiyong,et al.Coal and gas outburst prediction model based on improved GA-ELM［J］.Journal of Underground Space and Engineering,2019,15(6):1895-1902.
［8］谢国民,单敏柱,刘明.煤与瓦斯突出强度的FOA-SVM预测模型与应用［J］.传感技术学报,2016,29(12):1941- 1946. XIE Guomin,SHAN Minzhu,LIU Ming.FOA-SVM prediction model and application of coal and gas outburst intensity［J］.Journal of Sensing Technology,2016,29(12):1941-1946.
［9］陈娟,王献雨,罗玲玲,等.缺失值填补效果：机器学习与统计学习的比较［J］.统计与决策,2020,36(17):28-32. CHEN Juan,WANG Xianyu,LUO Lingling,et al.Missing value filling effect:A comparison between machine learning and statistical learning［J］.Statistics and Decision Making,2020,36(17):28-32.
［10］刘凤芹.基于链式方程的收入变量缺失值的多重插补［J］.统计研究,2009,26(1):71-77. LIU Fengqin.Multiple imputation of missing values of income variables based on chain equation［J］.Statistical Research,2009,26(1):71-77.
［11］宋亮,万建洲.缺失数据插补方法的比较研究［J］.统计与决策,2020,36(18):10-14. SONG Liang,WAN Jianzhou.Comparative study on missing data interpolation methods［J］.Statistics and Decision Making,2020,36(18):10-14.
［12］王刚,武猛猛,王海洋,等.基于能量平衡模型的煤与瓦斯突出影响因素的灵敏度分析［J］.岩石力学与工程学报,2015,34(2):238-248. WANG Gang,WU Mengmeng,WANG Haiyang,et al.Sensitivity analysis of influencing factors of coal and gas outburst based on energy balance model［J］.Journal of Rock Mechanics and Engineering,2015,34(2):238-248.
［13］郑晓亮.基于瓦斯含量法的煤与瓦斯突出预测关键技术研究［D］.淮南：安徽理工大学,2018.
［14］NUGROHO H,UTAMA N P,SURENDRO K.Class center-based firefly algorithm for handling missing data［J］.Journal of Big Data,2021,8(1):1-14.

相似文献/References:

[1]念其锋,施式亮,李润求.基于网络分析和联系熵的煤与瓦斯突出预测研究[J].中国安全生产科学技术,2014,10(2):22.[doi:10.11731/j.issn.1673-193x.2014.02.004]
　NIAN Qi feng,SHI Shi liang,LI Run qiu.Study on coal and gas outburst prediction based on analytic network process and connection entropy[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(7):22.[doi:10.11731/j.issn.1673-193x.2014.02.004]
[2]温廷新,高倩.基于AE-CLSSA-ELM的煤与瓦斯突出危险性预测模型*[J].中国安全生产科学技术,2023,19(5):73.[doi:10.11731/j.issn.1673-193x.2023.05.010]
　WEN Tingxin,GAO Qian.Prediction model of coal and gas outburst risk based on AE-CLSSA-ELM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(7):73.[doi:10.11731/j.issn.1673-193x.2023.05.010]
[3]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.[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(7):94.[doi:10.11731/j.issn.1673-193x.2023.08.014]
[4]邵良杉,毕圣昊,王彦彬,等.基于ISSA-ELM的煤与瓦斯突出危险等级预测*[J].中国安全生产科学技术,2023,19(9):76.[doi:10.11731/j.issn.1673-193x.2023.09.011]
　SHAO Liangshan,BI Shenghao,WANG Yanbin,et al.Prediction of coal and gas outburst risk level based on ISSA-ELM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(7):76.[doi:10.11731/j.issn.1673-193x.2023.09.011]

备注/Memo

备注/Memo:
收稿日期： 2021-12-09
*基金项目： 国家自然科学基金项目（71771111）；辽宁省社会科学规划基金项目（L14BTJ004）
作者简介： 温廷新，博士，教授，主要研究方向为矿业系统工程、数据分析与智能决策等。
通信作者： 苏焕博，硕士研究生，主要研究方向为矿业系统工程、数据分析等。
更新日期/Last Update: 2022-08-10