Research on the prediction model of coal spontaneous combustion temperature based on random forest algorithm
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摘要: 针对传统煤自燃温度预测模型预测精度较差、基于支持向量机（SVM）的预测模型对参数的选取要求较高和基于神经网络的预测模型测试时易出现过拟合的问题，提出了一种基于随机森林算法的煤自燃温度预测模型。利用煤自燃程序升温实验选取O2浓度、CO浓度、C2H4浓度、CO/ΔO2比值、C2H4/C2H6比值作为煤自燃预警指标数据，并对指标数据进行处理，将数据分为学习集和测试集；对学习集抽样形成决策树并按决策树最优特征分裂形成随机森林；采用均方误差值和判定系数（R2）优化随机森林算法的参数,进而构建随机森林模型；将测试集数据输入已训练好的随机森林模型,得到煤自燃温度预测结果。模型对比结果表明：与基于粒子群优化反向传播（PSO-BP）神经网络算法和基于SVM算法的煤自燃温度预测模型相比，随机森林测试阶段的R2为0.869 7，PSO-BP测试阶段的R2为0.783 6，SVM测试阶段的R2为0.835 0，说明基于随机森林算法的煤自燃温度预测模型能够较为准确地对煤自燃温度进行预测，具有较强的鲁棒性和普适性，解决了基于PSO-BP神经网络算法的煤自燃温度预测模型和基于SVM算法的煤自燃温度预测模型容易出现过拟合的问题。Abstract: The prediction accuracy of the traditional coal spontaneous combustion temperature prediction model is poor. The requirement of parameter selection for the prediction model based on support vector machine (SVM) is high. And neural network-based prediction model is prone to overfitting. In order to solve the above problems, a prediction model of coal spontaneous combustion temperature based on random forest algorithm is proposed. The model uses the coal spontaneous combustion temperature program experiment to select O2 concentration, CO concentration, C2H4 concentration, CO/ΔO2 ratio and C2H4/C2H6 ratio as coal spontaneous combustion warning index data, processes the index data and divides the data into learning set and test set. The learning set is sampled to form a decision tree and split according to the optimal characteristics of the decision tree to form a random forest. The parameters of the random forest algorithm are optimized by the mean square error value and the determination coefficient (R2) to construct the random forest model. The test set data is input into the trained random forest model to obtain the prediction result of coal spontaneous combustion temperature. The model comparison results show that compared with the coal spontaneous combustion temperature prediction model based on the particle swarm optimization-back propagation(PSO-BP) neural network algorithm and the support vector machine algorithm, the R2 value in the random forest test phase is 0.869 7, the R2 value in the PSO-BP test phase is 0.783 6, and the R2 value in the SVM test phase is 0.835 0. The results shows that the prediction model of coal spontaneous combustion temperature based on RF algorithm can predict coal spontaneous combustion temperature more accurately and has strong robustness and universality. The model solves the problem that the prediction model of coal spontaneous combustion temperature based on PSO-BP neural network algorithm and the prediction model of coal spontaneous combustion temperature based on SVM algorithm are prone to overfitting.
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