|本期目录/Table of Contents|

[1]曹占华,袁海平,李恒喆.基于PCA-PNN的采空区多源指标危险性辨识*[J].中国安全生产科学技术,2022,18(12):104-109.[doi:10.11731/j.issn.1673-193x.2022.12.014]
　CAO Zhanhua,YUAN Haiping,LI Hengzhe.Multi-source indexes risk identification of goaf based on PCA-PN[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(12):104-109.[doi:10.11731/j.issn.1673-193x.2022.12.014]
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基于PCA-PNN的采空区多源指标危险性辨识*
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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12期
页码:
104-109
栏目:
职业安全卫生管理与技术
出版日期:
2022-12-31

文章信息/Info

Title:
Multi-source indexes risk identification of goaf based on PCA-PN
文章编号:
1673-193X(2022)-12-0104-06
作者:
曹占华; 袁海平; 李恒喆
(合肥工业大学 土木与水利工程学院，安徽 合肥 230009)
Author(s):
CAO Zhanhua;  YUAN Haiping;  LI Hengzhe
(School of Civil and Hydraulic Engineering,Hefei University of Technology,Hefei Anhui 230009,China)
关键词:
采空区; 危险性评价; 主成分分析; 概率神经网络; 机器学习
Keywords:
goaf;  risk assessment;  principal component analysis (PCA);  probabilistic neural network (PNN);  machine learning
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.12.014
文献标志码:
A
摘要:
为了提高采空区多源指标危险性辨识的预测精度，基于主成分分析（PCA）和概率神经网络（PNN），提出1种采空区多源指标危险性辨识方法。将影响华东某地区矿山采空区危险性辨识的9项因素作为主要影响因素，并以96个实测采空区为例进行分级。研究结果表明:与朴素贝叶斯、随机森林和AdaBoost 3种机器学习算法相比，PNN在测试集上表现更好，对实际工程具有良好的指导意义和应用价值。
Abstract:
In order to improve the prediction accuracy for the multi-source index risk identification of goaf,based on the principal component analysis (PCA) and the probabilistic neural network (PNN),a kind of risk identification method of the multi-source indexes of goaf was proposed.9 factors affecting the risk identification of goaf in a region of east China were determined as the primary influencing factors,and 96 measured goafs were classified as examples.The results showed that compared with three machine learning algorithms of the Naive Bayes,the Random Forest and the AdaBoost,PNN performed more preferable on the test set,which has admirable guiding significance and application value for the practical engineering.

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备注/Memo

备注/Memo:
收稿日期： 2022-07-13
* 基金项目： 国家自然科学基金项目(51874112)
作者简介： 曹占华，硕士研究生，主要研究方向为矿山采空区智能算法。
更新日期/Last Update: 2023-01-16