瓦斯涌出量与开采环境和开采技术之间存在复杂的非线性关系,为了精准预测瓦斯涌出量,提出将非负矩阵分解(NMF)和随机森林(RF)相结合,建立煤层瓦斯涌出量预测模型。对沈阳某矿井数据的13个特征指标进行非负矩阵分解实现降维,将提取的5个主要成分作为RF的输入,瓦斯涌出量作为输出,实现回归预测。运用饥饿游戏搜索算法(HGS)对RF进行参数优化,并与麻雀搜索算法(SSA)、冠状病毒群体免疫优化算法(CHIO)、遗传算法(GA)、算数优化算法(AOA)、Aquila优化器算法(AO)、阿基米德算法(ArchOA)参数优化结果进行对比。实验结果显示:NMF-HGS-RF的收敛速度相对较快,预测精度相对较高,其相对误差的平均值为4.74%,而NMF-RF、NMF-SSA-RF、NMF-CHIO-RF等模型相对误差的平均值分别为6.34%、5.85%、6.21%、8.49%、4.95%、6.11%和7.93%。结果表明:NMF-RF预测精度高于未经降维处理的RF,且NMF-HGS-RF相比其他参数寻优后的RF具有更好的预测性能。

There is a complex nonlinear relationship between the gas emission rate and the mining environment and mining technology. In order to accurately predict the gas emission rate, a prediction model of gas emission rate of coal seam was established by combining non-negative matrix decomposition (NMF) and random forest ( RF). The dimension reduction of 13 characteristic indexes of the data from a mine in Shenyang was reduced by non-negative matrix decomposition. The extracted 5 main components were used as the inputs of RF, and the gas emission rate was used as the output to realize regression prediction. Hunger Games Search Algorithm (HGS) was used to optimize the parameters of RF, and the results were compared with those of Sparrow Search Algorithm (SSA), Coronavirus Herd Immunity Optimization (CHIO), Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Aquila Optimizer (AO) and Archimedes Algorithm (ArchOA). The experimental results show that the convergence speed of NMF-HGS-RF is relatively fast and the prediction accuracy is relatively high. The average relative error of NMF - HGS - RF is 4. 74%. The average relative errors of NMF-RF, NMF-SSA-RF, NMF-CHIO-RF and other models are 6. 34%, 5. 85%, 6. 21%, 8. 49%, 4. 95%, 6. 11% and 7. 93% respectively. The results show that the prediction accuracy of NMF-RF is higher than that of RF without dimension reduction, and NMF - HGS - RF has better prediction performance than RF after optimization of other parameters.