| 基于SVM的矿井通风阻力系数影响因素分析与预测 |
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| 引用本文： | 魏宁,孙亚胜男,邓立军,黄德,郭欣.基于SVM的矿井通风阻力系数影响因素分析与预测[J].中国安全生产科学技术,2018,14(4):39-44. |
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| 作者姓名： | 魏宁 孙亚胜男 邓立军 黄德 郭欣 |
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| 作者单位： | （1.辽宁工程技术大学 安全科学与工程学院，辽宁 葫芦岛 125105；2.矿山热动力灾害与防治教育部重点实验室，辽宁 葫芦岛 125105） |
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| 摘 要： | 矿井通风阻力系数是通风安全最重要的基础参数之一，为了实现矿井通风阻力系数简单准确地预测，提出了利用支持向量机（SVM）来预测矿井通风阻力系数的方法。通过分析影响因子与矿井通风阻力系数的相关性关系，并利用MATLAB逐步建立单影响因素与矿井通风阻力系数、多影响因素与矿井通风阻力系数之间的SVM预测模型，对比分析各预测模型的相对误差，确定最佳矿井通风阻力系数预测模型，即当输入模型影响因素为巷道断面积、周长和支护方式时，预测相对误差小于10%的样本数占测试样本的76%，相对误差小于20%的样本数占测试样本的90%。结果表明:该预测方法在矿井通风阻力系数预测中是可行的，并具较高的准确性。
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| 关 键 词： | 矿井通风 矿井通风阻力系数预测 影响因子 支持向量机（SVM） 相关性分析 |
Influence factors analysis and prediction on mine ventilation resistance coefficient based on SVM |
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| WEI Ning,,SUN Yashengnan,,DENG Lijun,,HUANG De,,GUO Xin,.Influence factors analysis and prediction on mine ventilation resistance coefficient based on SVM[J].Journal of Safety Science and Technology,2018,14(4):39-44. |
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| Authors: | WEI Ning SUN Yashengnan DENG Lijun HUANG De GUO Xin |
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| Affiliation: | (1.College of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125105,China; 2.Key Laboratory of Mine Thermo-motive Disaster and Prevention,Ministry of Education,Huludao Liaoning 125105,China) |
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| Abstract: | The mine ventilation resistance coefficient is one of the most important basic parameters of ventilation safety,and in order to realize the simple and accurate prediction of the mine ventilation resistance coefficient,a method to predict the mine ventilation resistance coefficient by using the support vector machine (SVM) was put forward. Through analyzing the correlation between the influence factors and the mine ventilation resistance coefficient,the SVM prediction models between the single influence factor and mine ventilation resistance coefficient and between the multiple influencing factors and mine ventilation resistance coefficient were established step by step by using MATLAB. The relative error of each prediction model were compared and analyzed,then the optimal prediction model of the mine ventilation resistance coefficient was determined,namely when the influence factors inputting the model were the sectional area,the perimeter and the support mode of roadway,the sample number with the relative error of prediction being less than 10% accounted for 76% of the test samples,and the sample number with the relative error of prediction being less than 20% accounted for 90% of the test samples. The results showed that this prediction method is feasible and accurate in the prediction of mine ventilation resistance coefficient. |
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| Keywords: | mine ventilation prediction of mine ventilation resistance coefficient influence factor support vector machine (SVM) correlation analysis |
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