基于LASSO-LARS的回采工作面瓦斯涌出量预测研究
Gas emission prediction from coalface based on Least Absolute Shrinkage and Selection Operator and Least Angle Regression
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摘要:
正确预测瓦斯涌出量对于煤矿安全生产有重要的现实意义，但是，工作面瓦斯涌出规律复杂，瓦斯涌出量各影响因素之间存在多重共线性，严重影响了预测的准确性。为研究回采工作面瓦斯涌出量与其多个影响因素之间的关系和特点，消除各因素之间的多重共线性，避免瓦斯涌出量预测出现“维数灾难”以及发生函数过拟合等问题，采用LASSO惩罚回归预测模型进行仿真预测，在原始特征空间的基础上，通过LARS算法实现降维，剔除无关和冗余的特征，最终筛选出一个包含煤层埋藏深度、煤层厚度、煤层瓦斯含量、煤层挥发分产率、风量和煤层间距等6个高影响因素在内的最优特征子集，并使用交叉验证法将数据集分成10份，轮流将其中9份作为训练数据，1份作为测试数据，进行试验。最终，选取最高识别率的测试集参数建立预测模型，对煤矿现场数据进行预测，并与传统的主成分分析法预测结果进行了比较。研究结果表明：应用该模型预测回采工作面瓦斯涌出量，能够较好的保存原始数据集的特征意义，预测平均相对误差为6.52%，平均相对变动值为0.006，均方根误差为3.20，在预测精度和泛化能力方面，均明显优于传统的主成分分析回归模型，能够为井下瓦斯防治提供理论参考，对其他工程领域高维小样本数据预测问题的解决具有借鉴意义。
Abstract:The correct prediction of gas emission has great practical significance for the safety production of coal mine. However, the law of gas emission in working face is complex, and there is multicollinearity among the influencing factors of gas emission volume, which seriously affects the accuracy of prediction. In order to study the relationship and characteristics between gas emission and its influencing factors, eliminate the multi-collinearity among these various factors, avoid “dimension disaster” and the over-fitting in the prediction of gas gushing volume, the least absolute shrinkage and selection operator(Lasso)penalized regression method was adopted for simulation prediction. On the basis of the original feature space, the Least Angle Regression(LARS)algorithm was used to achieve dimensionality reduction, eliminate irrelevant and redundant features, and finally screen out 6 high-influencing factors including coal seam depth, coal seam thickness, coal seam gas content, coal seam volatile yield, air capacity and coal seam spacing, etc.. The data set was then divided into ten parts by using the cross-validation method, and 9 of them were used as training data and one was used as test data in turn. Finally, the parameters of the test set with the highest recognition rate were selected to establish a prediction model to predict the coal mine field data. Finally, Lasso method was compared with traditional principal component analysis prediction model. The study results show that Lasso penalty regression model can better preserve the characteristic meaning of the original data set. and the mean relative error is 6.52%, the mean relative change value is 0.006, and the root mean square error is 3.20, which are superior to the results of principal component analysis regression model, proving that Lasso model has better prediction accuracy and stronger generalization ability. It can provide a theoretical reference for downhole gas prevention and control, and has important reference significance for solving the problem of high-dimensional and small-sample data prediction in other engineering fields.
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Keywords:
- gas emission /
- feature selection /
- regression algorithm /
- prediction model /
- gas emission prediction
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期刊类型引用(8)
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百度学术
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其他类型引用(2)
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