Prediction of gas emission in mining face based on random forest regression algorithm
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摘要:
回采工作面是矿井瓦斯涌出的主要场所，精准预测回采工作面的瓦斯涌出量，进而有针对性地提出防治措施，对保证矿井安全生产具有重要意义。提出了基于随机森林回归算法的回采工作面瓦斯涌出量预测方法。以工作面实测瓦斯涌出量数据为原始样本，利用Bootstrap抽样方法进行随机抽样，以袋外数据（OOB）评估分数oob_score作为随机森林回归模型调参、特征变量重要性的评判指标，计算得出模型的最佳参数、特征变量重要性占比。对各特征变量的重要性占比进行排序，并按排序进行随机森林回归模型性能分析，结果表明：随着特征变量数的增加，模型性能不会呈现规律性的变化；当特征变量数较少时，可能存在过拟合的情况。测试结果表明，所创建的随机森林回归模型预测值与实测值的平均绝对误差、平均相对误差随着特征变量数的增加呈下降趋势，特征变量数的增加可在一定程度上提高模型的预测效果。针对同一组数据，与主成分回归分析法相比，随机森林回归模型平均相对误差降低了14.29%，预测效果更好，且原理更简单、调参更容易、计算速度更快，能够为矿井回采工作面瓦斯涌出量预测提供有力的理论支撑。
Abstract:The mining face is the main place for gas emission in mines. Accurately predicting the amount of gas emission from the mining face and proposing targeted prevention and control measures are of great significance for ensuring mine safety production. A prediction method for gas emission in mining face based on random forest regression algorithm has been proposed. Using the measured gas emission data from the working face as the original sample, the Bootstrap sampling method is used for random sampling. The out-of-bag (OOB) data assessment score oob_score is used as an evaluation indicator for the random forest regression model tuning parameter and importance of feature variables. The optimal parameters of the model and the percentage of importance of feature variables are calculated. The method ranks the importance proportion of each feature variable and conducts performance analysis of the random forest regression model according to the ranking. The results show that as the number of feature variables increases, the model performance does not show a regular change. When the number of feature variables is small, there may be overfitting. The test results show that the average absolute error and relative error between the predicted and measured values of the created random forest regression model decrease with the increase of the number of feature variables. The increase of the number of feature variables can improve the predictive performance of the model to a certain extent. Compared with the principal component regression analysis method, the random forest regression model reduces the average relative error by 14.29% for the same set of data, resulting in better prediction performance. The principle is simpler, parameter adjustment is easier, and the calculation speed is faster. The results can provide strong theoretical support for predicting gas emission in mining face.
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图 2 随机森林回归模型参数关系热力图
Figure 2. Thermodynamic diagram of random forest regression model parameter relationship
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图 3 n_estimators与obb_score的关系曲线
Figure 3. The relationship curves of n_estimators and obb_score
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图 5 特征变量数与oob_score关系
Figure 5. The relationship between the number of characteristic variables and oob_score
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图 6 特征变量数与误差平均值关系曲线
Figure 6. The relationship between the number of characteristic variables and mean value of error
表 1 回采工作面瓦斯涌出量特征样本数据
Table 1 Sample data of gas emission characteristics in the mining face
序号 X1/（m³·t−1） X2/m X3/m X4/（°） X5/m X6/m X7/m X8 X9/t X10 X11/（m³·t−1） X12/m X13/m X14 Y/（m³·min−1） 1 1.92 408 2.0 10 2.0 4.42 155 0.96 1825 1 2.02 1.5 20 5.03 3.34 2 2.15 411 2.0 8 2.0 4.16 140 0.95 1527 1 2.1 1.21 22 4.87 2.97 3 2.14 420 1.8 11 1.8 4.13 175 0.95 1751 1 2.64 1.62 19 4.75 3.56 4 2.58 432 2.3 10 2.3 4.67 145 0.95 2078 1 2.4 1.48 17 4.91 3.62 5 2.40 456 2.2 15 2.2 4.51 160 0.94 2104 1 2.55 1.75 20 4.63 4.17 6 3.22 516 2.8 13 2.8 3.45 180 0.93 2242 1 2.21 1.72 12 4.78 4.6 7 2.80 527 2.5 17 2.5 3.28 180 0.94 1979 1 2.81 1.81 11 4.51 4.92 8 3.35 531 2.9 9 2.9 3.68 165 0.93 2288 1 1.88 1.42 13 4.82 4.78 9 3.61 550 2.9 12 2.9 4.02 155 0.92 2352 1 2.12 1.6 14 4.83 5.23 10 3.68 563 3.0 11 3.0 3.53 175 0.94 2410 1 3.11 1.46 12 4.53 5.56 
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表 2 随机森林回归模型主要待调参数
Table 2 The main parameters to be adjusted in the random forest regression model
序号 参数 参数说明 1 n_estimators 随机森林中决策树的数量 2 criterion 回归树衡量回归质量的指标 3 random_state 生成的森林模式 4 max_features 最佳分支时的特征个数 5 max_depth 决策树剪枝参数，防止模型过拟合。本次
原始样本数量较少，不进行剪枝Min_sample_leaf Min_sample_spit Min_impurity_decrease
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表 3 随机森林回归模型调参结果
Table 3 Parameter adjustment results of random forest regression model
criterion n_estimators max_
featuresrandom_state 最大obb_score mse 23 11 165 0.91575566 mae 20 14 70 0.92116429 friedman_mse 34 14 34 0.91395423
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表 4 随机森林回归模型预测误差
Table 4 Prediction error of random forest regression model
特征变量个数 平均绝对误差/（m³·min−1） 平均相对误差/% 3 0.228 5.030 4 0.174 3.72 5 0.109 3.21 6 0.091 2.36 7 0.090 2.32 8 0.147 3.38 9 0.124 3.05 10 0.083 2.15 11 0.053 1.72 12 0.109 2.73 13 0.026 1.28 14 0.005 0.77
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表 5 不同预测模型预测结果对比
Table 5 Comparison of prediction results of different prediction models
实测值/
（m³·min−1）随机森林回归模型 主成分回归分析法 预测值/
（m³·min−1）绝对误差/
（m³·min−1）相对误差/% 预测值/
（m³·min−1）绝对误差/
（m³·min−1）相对误差/% 4.06 3.85 −0.21 5.17 4.01 −0.05 1.23 4.92 4.84 −0.08 1.63 5.30 0.38 7.72 8.04 7.56 −0.48 5.97 7.56 −0.48 5.97
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