基于随机森林回归算法的回采工作面瓦斯涌出量预测

张增辉; 马文伟

doi:10.13272/j.issn.1671-251x.2023020006

基于随机森林回归算法的回采工作面瓦斯涌出量预测

doi: 10.13272/j.issn.1671-251x.2023020006

张增辉^1,,
马文伟^{2, 3, 4}

1.
国家能源集团神东煤炭集团有限责任公司保德煤矿，山西忻州　036600
2.
中煤科工集团沈阳研究院有限公司，辽宁抚顺　113122
3.
煤矿安全技术国家重点实验室，辽宁抚顺　113122
4.
西安交通大学人居环境与建筑工程学院，陕西西安　710049

基金项目: 国家科技重大专项资助项目（2016ZX05045-004-001）。

详细信息

作者简介:
张增辉（1987—），男，河南西华人，工程师，硕士，研究方向为井工煤矿灾害治理，E-mail：544396324@qq.com

中图分类号: TD712
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出版历程
- 收稿日期: 2023-07-22
- 修回日期: 2023-12-10
- 网络出版日期: 2024-01-03

Prediction of gas emission in mining face based on random forest regression algorithm

ZHANG Zenghui^1
,,
MA Wenwei^{2, 3, 4}

1.
Baode Coal Mine, CHN Energy Shendong Coal Group Co., Ltd., Xinzhou 036600, China
2.
CCTEG Shengyang Research Insititute, Fushun 113122, China
3.
State Key Laboratory of Coal Mine Safety Technology, Fushun 113122, China
4.
School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China

摘要

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。
- 回采工作面 /
- 瓦斯涌出量预测 /
- 随机森林回归 /
- 袋外数据评估分数 /
- 特征变量重要性 /
- 特征变量数
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.
- mining face /
- gas emission prediction /
- random forest regression /
- out of bag data assessment score /
- importance of feature variables /
- number of feature variables

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图 1 随机森林回归算法原理

Figure 1. Principle of random forest regression algorithm

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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 4 特征变量重要性占比

Figure 4. The proportion of importance of characteristic variables

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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

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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_ features	random_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
实测值/ （m³·min⁻¹）	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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