|本期目录/Table of Contents|

[1]陈利成,陈建宏.基于数据填补-机器学习的煤与瓦斯突出预测效果研究*[J].中国安全生产科学技术,2022,18(9):69-74.[doi:10.11731/j.issn.1673-193x.2022.09.010]
　CHEN Licheng,CHEN Jianhong.Study on prediction effect of coal and gas outburst based on data imputation and machine learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(9):69-74.[doi:10.11731/j.issn.1673-193x.2022.09.010]
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基于数据填补-机器学习的煤与瓦斯突出预测效果研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年9期
页码:
69-74
栏目:
职业安全卫生管理与技术
出版日期:
2022-09-30

文章信息/Info

Title:
Study on prediction effect of coal and gas outburst based on data imputation and machine learning
文章编号:
1673-193X(2022)-09-0069-06
作者:
陈利成; 陈建宏
（中南大学 资源与安全工程学院，湖南 长沙 410083）
Author(s):
CHEN Licheng;  CHEN Jianhong
(School of Resources and Safety Engineering,Central South University,Changsha Hunan 410083,China)
关键词:
煤与瓦斯突出; 预测; 多重插补（MI）; 随机森林填补（MF）; 机器学习
Keywords:
coal and gas outburst;  prediction;  multiple imputation (MI);  MissForest (MF);  machine learning
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.09.010
文献标志码:
A
摘要:
为解决煤与瓦斯突出事故数据集少，数据缺失严重的问题，提出将多重插补（MI）和随机森林填补(MF)应用于填补缺失参数，并将填补前和填补后的数据输入SVM，ELM，RF 3种机器学习算法进行训练，构建9种耦合模型。采用总体准确率、局部准确率、运行时间这3种指标评价模型性能。研究结果表明：采用数据填补算法后，由于训练样本增大，煤与瓦斯突出事故预测的总体准确率提高，运行时间增长；MF-RF模型的总体准确率与事故预测准确率最高，分别为97.90%和98.93%；RD-ELM模型的运行时间最短，为0.24 s；多重插补使得煤与瓦斯突出预测的总体准确率提高0.98%~1.11%，随机森林填补总体准确率提高5.13%~7.50%，随机森林填补的效果好于多重插补。
Abstract:
In order to solve the problem of less data sets and serious data missing in the coal and gas outburst accidents,the multiple imputation (MI) and MissForest (MF) were applied to fill the missing parameters.The data before and after imputation were input into three machine learning algorithms of SVM,ELM and RF for training,then nine coupling models were constructed,and the overall accuracy,local accuracy and running time were used to evaluate the performance of the models.The results showed that after using the data imputation algorithms,the overall accuracy of coal and gas outburst accident prediction improved,and the running time increased due to the increase of training samples.The overall accuracy and accident prediction accuracy of MF-RF model were the highest,which was 97.90% and 98.93%,respectively.The running time of RD-ELM model was the shortest as 0.24 s.The MI improved the overall accuracy of coal and gas outburst prediction by 0.98%~1.11%,and the overall accuracy of MF increases by 5.13%~7.50%.The effect of MF was better than that of MI.

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备注/Memo

备注/Memo:
收稿日期： 2021-10-25
* 基金项目： 国家自然科学基金项目（51374242）
作者简介： 陈利成，硕士研究生，主要研究方向为安全预警与应急管理。
更新日期/Last Update: 2022-10-14