基于随机森林与支持向量机的回采工作面瓦斯涌出量预测方法
Prediction method of gas emission based on random forest and support vector machine
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摘要: 针对当前回采工作面瓦斯涌出量预测方法选取不合理及预测精度不佳问题，借助机器学习方法对其展开研究，以交叉验证法处理现有数据，构建了支持向量回归模型与随机森林回归预测模型，并对输入数据降维前后2种模型的预测表现进行了对比分析。测试表明：经交叉验证及数据降维后2种预测模型趋于稳定、预测精度均有大幅提高，SVR模型的整体预测性能稍优于RFR模型，SVR模型每轮测试耗时约0.015 s，远低于后者；筛选后的SVR模型平均绝对误差约为0.18 m3/min，平均相对误差约3.26%，优于RFR预测模型；建立的回采工作面瓦斯涌出量预测模型均表现良好，经随机森林筛选的SVR预测模型耗时较短、误差较小、稳定性更好，可实现对瓦斯涌出量的有效预测，对简化数据采集工作及科学制定瓦斯防治措施起到参考作用。Abstract: In order to solve the problems of unreasonable selection of prediction methods and poor prediction accuracy for gas emission in mining face, with the help of machine learning methods, in this paper, existing data was processed through cross-validation, the support vector and the random forest algorithm were used to construct the prediction model and the prediction performance of the two models before and after dimensionality reduction of the input data were compared and analyzed. The test shows that after cross-validation and data dimensionality reduction, the two prediction models tend to be stable, and the prediction accuracy are greatly improved. The overall prediction performance of the SVR model is slightly better than that of the RFR model; SVR model takes about 0.015 s per round of testing, much lower than the latter one; the average absolute error of the SVR model is about 0.18 m3/min, and the average relative error is about 3.26%, which is relatively better than the other one. The models established in this paper both present good performance, and the SVR model after feature screening by random forest takes less time, has less error and better stability, which effectively realizes the prediction of the gas emission and plays a reference role in simplifying data collection and scientifically formulating gas control measures.
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