基于随机森林的回采巷道围岩稳定性分类

Classification of Roadway Surrounding Rock Stability Based on Random Forest

根据回采巷道稳定性的影响因素,选取围岩强度、埋深、节理裂隙发育程度、巷道跨度、直接顶与煤层厚度之比和松动圈厚度6个指标作为巷道稳定性识别的样本变量。通过搜集部分矿井35条回采巷道相关数据,采用随机森林建立回采巷道稳定性分类模型,并将该模型的预测效果与决策树、BP神经网络和支持向量机模型进行对比。研究结果表明:采用随机森林模型误判率低,具有较高的预测精度,能够相对有效地对回采巷道的稳定性进行判定。

In the article, rock strength, depth, fracture development, width of roadway, thickness ratio of immediate roof and coal seam, thickness of the loosen zone are selected as sample variables based on roadway stability influencing factors. Through related - data of 35 roadways collected in some coal mines, roadway stability classification model is established by random forest, and the predic- tion result is compared with the decision tree, BP neural networks and support vector machines model. The results show that random torest model can relatively and effectively determine the stability of roadway with low false rate and high prediction accuracy.