基于数据重构增强的采空区遗煤自燃预测模型

Natural prediction model of goaf residual coal based on data reconstruction and enhancement

目前采空区遗煤自燃预测模型的外推泛化能力不强,主要原因是模型训练数据集数量较少以及监测过程和方法等造成的数据分布特征不明显;采用WGAN-GP模型生成虚拟样本,对实测数据集特征分布进行重构增强;通过参数相关性计算,生成虚拟数据集各参数间相关性系数变化均未超过15%;采用1倍实测数据集数量的扩容虚拟样本,进行模型的学习训练。结果表明:各模型的预测性能均有提高,其中R~2指标GA-BPNN模型提高12%,GA-SVM模型提高4%,RF模型提高3%;MAE指标均降低,GA-BPNN模型降低0.67℃,GA-SVM模型降低了0.54℃,RF模型降低0.33℃;RMSE指标均降低,GA-BPNN模型降低0.41℃,GA-SVM模型降低0.46℃,RF模型降低0.39℃;增强扩容的数据集对3种预测模型的性能都有提高,其中GABPNN模型预测性能提高幅度最大。

The prediction model of residual coal spontaneous combustion temperature in goaf is weak of extrapolation and generalization at present. The main reason is that the number of model training data sets is small and the data distribution characteristics are not obvious due to the monitoring process and methods. In this paper, the WGAN-GP model is used to generate virtual samples and reconstruct and enhance the measured data sets. Through parameter correlation calculation, the variation of correlation coefficient among parameters of the generated virtual data set is not more than 15%. The results show that the prediction performance of each model is improved, among which the R~2 index GA-BPNN model is improved by 12%, GA-SVM model is improved by 4%, RF model is improved by 3%. MAE index was decreased by 0.67 ℃ for GA-BPNN model, 0.54 ℃ for GA-SVM model and 0.33 ℃ for RF model. The RMSE index was decreased by 0.41 ℃ in GA-BPNN model, 0.46 ℃ in GA-SVM model and0.39 ℃ in RF model. The enhanced and expanded data sets can improve the performance of the three prediction models, among which GA-BPNN model has the greatest improvement.