基于SSA-RF的采空区煤自燃温度回归分析模型

Regression analysis model of coal spontaneous combustion temperature in goaf based on SSA-RF

为了快速精准地对采空区遗煤自燃温度进行回归分析,避免自燃火灾发生,提出麻雀搜索算法(SSA)与随机森林(RF)算法相结合的SSA-RF采空区煤自燃温度回归分析模型。首先,基于东滩矿煤自燃特性试验获得的数据,对比分析SSA-RF模型与RF、反向传播神经网络(BPNN)、粒子群算法(PSO)-BPNN、SSA-BPNN模型的回归结果;然后以正佳煤业1204采煤工作面的试验数据为例,验证SSA-RF模型的可靠性;最后将该模型应用于东古城煤矿。结果表明:SSA-RF、RF、BPNN、PSO-BPNN以及SSA-BPNN模型测试样本的平均绝对误差(MAE)分别为11.2031、14.3420、19.5991、15.5306、14.3528;平均绝对百分比误差(MAPE)分别为14.89%、16.91%、18.55%、18.43%、18.11%;均方根误差(RMSE)分别为13.7610、16.5250、20.7866、18.0227、17.7355;决定系数(R 2)分别为0.9274、0.8827、0.8153、0.8436、0.8688;其中SSA-RF模型各指标均为最优,说明其具有普适性和稳定性,更适合煤自燃温度回归分析。

In order to accurately and quickly analyze the spontaneous combustion temperature of coal in goaf and avoid spontaneous combustion fire,the SSA-RF regression analysis model combining SSA and RF algorithm was proposed.Firstly,based on the data obtained from the spontaneous combustion characteristics test in Dongtan coal mine,the regression results of SSA-RF model and RF,back propagation neural network(BPNN),particle swarm optimization algorithm(PSO)-BPNN and SSA-BPNN model were compared and analyzed.Then,the reliability of the SSA-RF model was verified by taking the test data of 1204 coal face in Zhengjia coal mining as an example.Finally,the model was applied to Donggucheng coal mine.The results show that the mean absolute errors(MAE)of SSA-RF,RF,BPNN,PSO-BPNN and SSA-BPNN are 11.2031,14.3420,19.5991,15.5306 and 14.3528,respectively.The mean absolute percentage error(MAPE)is 14.89%,16.91%,18.55%,18.43%and 18.11%,respectively.The root mean square errors(RMSE)are 13.7610,16.5250,20.7866,18.0227 and 17.7355,respectively.The coefficients of determination(R 2)are 0.9274,0.8827,0.8153,0.8436 and 0.8688,respectively.All indexes of SSA-RF model are the best,which indicates that it is universal and stable,and it is more suitable for regression analysis of coal spontaneous combustion temperature.