熊欣标,谢雄刚,杨培君,等. 基于PCA-Logistic回归模型的矿井底板突水危险性研究[J]. 煤矿安全,2023,54(10):176−181. doi: 10.13347/j.cnki.mkaq.2023.10.023
    引用本文: 熊欣标,谢雄刚,杨培君,等. 基于PCA-Logistic回归模型的矿井底板突水危险性研究[J]. 煤矿安全,2023,54(10):176−181. doi: 10.13347/j.cnki.mkaq.2023.10.023
    XIONG Xinbiao, XIE Xionggang, YANG Peijun, et al. Research on water inrush risk of mine floor based on PCA-Logistic regression model[J]. Safety in Coal Mines, 2023, 54(10): 176−181. doi: 10.13347/j.cnki.mkaq.2023.10.023
    Citation: XIONG Xinbiao, XIE Xionggang, YANG Peijun, et al. Research on water inrush risk of mine floor based on PCA-Logistic regression model[J]. Safety in Coal Mines, 2023, 54(10): 176−181. doi: 10.13347/j.cnki.mkaq.2023.10.023

    基于PCA-Logistic回归模型的矿井底板突水危险性研究

    Research on water inrush risk of mine floor based on PCA-Logistic regression model

    • 摘要: 为解决煤层底板突水预测难题,提出了基于主成分分析与Logistic回归方法的底板突水预测模型。通过对底板突水危险因素进行分析,选取隔水层厚度、承压水水压、断层落差、断层距工作面距离、煤层采高、煤层倾角6个变量作为研究矿井底板突水的初始影响指标;首先利用主成分分析法对原始指标数据进行降维处理,然后利用建立的Logistic回归模型对数据进行分析预测,最后利用5组待测样本数据对模型进行验证。结果表明:该模型对突水样本的综合预判正确率为90%,利用待测数据进行回判时预测准确率达到80%,说明该预测模型具有一定可靠性,可作为煤矿底板突水预测的一种新方法。

       

      Abstract: In order to solve the prediction problem of water inrush from coal seam floor, a prediction model of water inrush from coal seam floor based on principal component analysis and Logistic regression method was proposed. Through the analysis of the risk factors of floor water inrush, six variables, including the thickness of the aquifers, the pressure of confined water, the fall of the fault, the distance between the fault and the working face, the mining height of the coal seam, and the dip angle of the coal seam, are selected as the initial impact indicators to study the floor water inrush. First, the principal component analysis method is used to reduce the dimensions of the original index data, and the established Logistic regression model is used to analyze and predict the data. Then, five groups of sample data to be tested are used to verify the model. The results show that the comprehensive prediction accuracy of the model for water inrush samples reaches 90% at the highest, and the prediction accuracy reaches 80% when the data to be measured is used for back judgment, which shows that the prediction model has certain reliability and can be used as a new method for predicting water inrush from coal mine floor.

       

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