露天煤矿边坡稳定性的随机森林预测模型

 边坡工程是露天煤矿中的重点工程，边坡的稳定性关系着煤矿的安全生产。边坡稳定性预测是边坡防治工作的前提，针对煤矿边坡工程稳定性预测的复杂性，为了快速、有效地判别煤矿边坡稳定性，利用随机森林算法建立煤矿边坡稳定性预测模型。通过选取与煤矿边坡工程密切相关的岩石重度、黏聚力、内摩擦角、边坡角、边坡高度、孔隙水压力6 个指标作为边坡稳定性的影响因素，即为随机森林预测模型的输入，边坡稳定性状态作为随机森林预测模型的输出，通过随机森林算法建立边坡稳定性影响因素与边坡稳定状态之间的非线性关系。利用煤矿实测30 组边坡稳定性数据作为随机森林预测模型的训练数据集，进行模型的学习训练；另用12 组边坡稳定性数据作为预测模型的测试数据，通过训练好的边坡稳定性预测模型进行测试；为了验证随机森林预测模型的准确率，同时与SVM 和BP 神经网络的测试数据进行比较。结果说明，选取煤矿边坡稳定性的6 个指标建立的随机森林预测模型，人工控制参数较少、结构简单、容易实现，且具有较高的准确度，边坡稳定状态预测结果与煤矿边坡工程实际状态相吻合，能有效预测边坡稳定性状态，指导煤矿边坡防治工作的开展。

Prediction Model for Open-pit Coal Mine Slope Stability Based on Random Forest

Slope engineering is a key project in open-pit coal mines. The stability of the slope is closely related to safety production of coal mines. Slope stability prediction is a prerequisite in slope control, faced with complexities. To quickly and effectively determine the coal mine slope stability, this paper establishes a prediction model using the random forest algorithm. Six factors influencing the slop stability were selected as input of the prediction model, including the gravity density of rocks, cohesive force, internal friction angle, slope angle, slope height and pore water pressure, and slope stability status was selected as output of the prediction model. The random forest algorithm was used to establish the nonlinear relationship between slope stability factors and stability status. The 30 sets of measured data were used as training data set to learn and train the random forest slope stability prediction model. In addition, 12 groups of data as slope stability test data were used to test the trained prediction models. In the meantime, the accuracy of the random forest prediction models was tested by comparing them with the SVM and BP neural network prediction models. The results show that the random forest prediction model based on the selected six factors has less manual control parameters, simple structure and high accuracy. The predictive results coincide with the actual state of the slope project, indicating that the prediction model is able to predict the slope stability effectively and provide guidance to coal mine slope prevention work.