煤层底板突水危险性GSPCA-LSSVM评价模型

doi:10.16265/j.cnki.issn1003-3033.2018.02.022

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (2): 128-133.doi: 10.16265/j.cnki.issn1003-3033.2018.02.022

煤层底板突水危险性GSPCA-LSSVM评价模型

赵琳琳¹ 讲师, 温国锋¹ 教授, 邵良杉² 教授

1 山东工商学院管理科学与工程学院,山东烟台 264005;
2 辽宁工程技术大学系统工程研究所,辽宁葫芦岛 125000

收稿日期:2017-11-20 修回日期:2018-01-16 出版日期:2018-02-28 发布日期:2020-11-17
作者简介:赵琳琳 (1988—),女,吉林榆树人,博士,讲师,主要从事矿山生产灾害智能预测,数据挖掘等方面的科研与教学工作。E-mail:linlinzhao1204@126.com。
基金资助:
国家自然科学基金资助(71771111,71371091)。

GSPCA-LSSVM model for evaluating risk of coal floor groundwater bursting

ZHAO Linlin¹, WEN Guofeng¹, SHAO Liangshan²

1 School of Management Science and Engineering, Shandong Technology and Business University, Yantai Shandong 264005, China;
2 System Engineering Institute, Liaoning Technical University, Huludao Liaoning 125000, China

Received:2017-11-20 Revised:2018-01-16 Online:2018-02-28 Published:2020-11-17

摘要/Abstract

摘要： 为快速、准确地评价煤层底板突水危险性,选取水压等作为影响因素,以其灰色关联度作为灰色主成分分析(GSPCA)的协方差矩阵,提取信息不重叠的灰色主成分,并将该成分作为最小二乘支持向量机(LSSVM)的输入向量,底板突水危险性作为LSSVM输出向量,建立煤层底板突水危险性GSPCA-LSSVM评价模型;将20组实测数据作为训练样本训练模型,采用回代估计法进行回检;利用训练好的模型对5组检验样本进行评价。结果表明:利用GSPCA提取的主成分考虑原影响因素不完备性,包含其超过91.97%的信息,减少信息冗余;经GSPCA处理后LSSVM计算复杂度降低;用GSPCA-LSSVM模型评价煤层底板突水危险性,结果与实际情况基本吻合。

关键词: 危险性评价, 底板突水, 灰色系统(GS), 主成分分析(PCA), 最小二乘支持向量机(LSSVM)

Abstract: In order to evaluate the risk of coal floor groundwater bursting quickly and accurately, quantities such as water pressre and excavation height were identified as factors influencing the bursting, gray relative correlations between the factors were used for covariance matrix of GSPCA, and gray principal components were obtained with nonoverlapping informations by GSPCA. A GSPCA-LSSVM evaluation model was built, which took the gray principal components as inputs and risks of coal floor groundwater bursting as outputs. The GSPCA-LSSVM model was trained through twenty groups of learning samples, and verified by the re-substitution method. The trained model was used to evaluate five groups of test samples. The results show that more than 91.97% of information of original factors has been extracted by GSPCA, which considers the incompletenes, that the redundant information and computation complexity have been reduced significantly, that the evaluation results obtained by using the model accord with the actual situation basically, and that the model could be used to evaluate the risk of coal floor groundwater bursting effectively.

Key words: risk assessment, water inrush, grey system(GS), principal component analysis(PCA), least square support vector machine(LSSVM)

中图分类号:

X936

赵琳琳, 温国锋, 邵良杉. 煤层底板突水危险性GSPCA-LSSVM评价模型[J]. 中国安全科学学报, 2018, 28(2): 128-133.

ZHAO Linlin, WEN Guofeng, SHAO Liangshan. GSPCA-LSSVM model for evaluating risk of coal floor groundwater bursting[J]. China Safety Science Journal, 2018, 28(2): 128-133.

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煤层底板突水危险性GSPCA-LSSVM评价模型

GSPCA-LSSVM model for evaluating risk of coal floor groundwater bursting

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