基于信息增益优化支持向量机模型的煤矿瓦斯爆炸风险预测
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TD76

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国家自然科学(71861021);甘肃省高等学校科研项目(2018A-026);甘肃省重点研发项目(17YF1FA122)


Risk prediction of coal mine gas explosion based on ig-svm model
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    摘要:

    为了探索基于样本数据的煤矿瓦斯爆炸风险预测,本文依据本质安全理念构建了预测瓦斯爆炸风险的指标集,结合机器学习与特征优化算法提出了IG-SVM的组合模型,通过对优化后的14种特征信息的分类学习,完成对风险未知样本的预测任务。以全国100家煤矿企业为研究对象,使用不同模型分别预测瓦斯爆炸风险并全面分析和比较,实验结果表明,经过IG优化后的SVM模型预测正确率达到了95.45%,相对于单一SVM模型提高了9.09%,同时高于其它预测模型,证明了该组合模型在瓦斯爆炸风险预测领域的优越性。

    Abstract:

    In order to explore the risk prediction of coal mine gas explosion based on the sample data, an index set for predicting gas explosion risk based on intrinsic safety concept is established, An immune support vector machine combined with machine learning and feature optimization algorithm is proposed, the prediction task of unknown risk samples was completed through the classification learning of 14 optimized feature information. 100 coal mining companies across the country were selected as the research objects, Different models are used to predict the risk of gas explosion, and are comprehensively analyzed and compared. The experimental results show that the accuracy of SVM model after IG optimization reaches 95.45%, Compared with the single SVM model, it is increased by 9.09%, and higher than other prediction models, which proves the superiority of the combined model in the field of gas explosion risk prediction.

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万宇,齐金平,张儒,等. 基于信息增益优化支持向量机模型的煤矿瓦斯爆炸风险预测[J]. 科学技术与工程, 2021, 21(9): 3544-3549.
Wan Yu, Qi Jinping, Zhang Ru, et al. Risk prediction of coal mine gas explosion based on ig-svm model[J]. Science Technology and Engineering,2021,21(9):3544-3549.

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历史
  • 收稿日期:2020-07-01
  • 最后修改日期:2021-01-11
  • 录用日期:2020-10-24
  • 在线发布日期: 2021-04-19
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