为提高瓦斯涌出量预测精度，降低煤矿回采工作面瓦斯涌出超限事故的风险，针对瓦斯涌影响因素众多、难以预测等问题，采用灰狼优化算法(Grey Wolf Optimization，GWO)-双向长短期记忆神经网络(Bi-directional Long Short-Term Memory，BiLSTM)的组合模型预测瓦斯涌出量。首先，运用主成分分析法(Principal Components Analysis，PCA)处理瓦斯涌出影响因素，降低数据维度，以减少模型计算时的负担;其次，利用GWO优化BiLSTM模型的学习率(best_lr)、隐藏层层数(best_hd)以及正则化系数(best_l2)，可有效避免局部最优解问题，并采用决定系数(R-Square，R2)、均方根误差(Root Mean Square Error，RMSE)和平均绝对误差(Mean Absolute Error，MAE)对所建模型预测的结果进行综合评价分析;最后，将该模型应用于内蒙古自治区某矿回采工作面预测瓦斯涌出量。结果显示:PCA-GWO-BiLSTM组合模型相比于长短期记忆神经网络(Long Short-Term Memory，LSTM)和双向长短期记忆神经网络对应的单一模型，其MAE分别降低 20。81％、30。17％，RMSE 分别降低 0。063、0。142，R2则分别提高了 0。023、0。075，表明该模型在复杂因素条件下具有更高的精准度、泛化性和鲁棒性。
Enhanced Bi-directional Long Short-Term Memory neural network for gas emission forecasting
In the context of coal mine safety management,this paper delves into the factors influencing gas emission exceeding limit accidents in coal mine working faces to enhance the accuracy of gas emission prediction and mitigate associated risks.Subsequently,a combined prediction model is formulated based on the principles of the Grey Wolf Optimization Algorithm(GWO)and the Bi-directional Long Short-Term Memory Neural Network(BiLSTM).Firstly,an analysis of relevant literature identified 13 factors that influence gas outbursts.Principal Component Analysis(PCA)was then applied to process the sample data.This involved normalizing the original sample data and combining it with the component matrix and variance contribution rate.These calculations were used to determine the three main factors that influence gas outbursts.This reduced the dimensionality of the data and alleviated the computational burden on the model.Secondly,the GWO algorithm was employed to optimize the learning rate(best_lr),number of hidden layers(best_hd),and regularization coefficient(best_l2)of the BiLSTM model.This approach helped identify the hyperparameters that are most suitable for the BiLSTM model,effectively mitigating the issue of local optimal solutions.The prediction results of the constructed model are thoroughly evaluated and analyzed using various metrics,including the coefficient of determination(R2),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and relative error.Finally,the model will be utilized to forecast gas emissions from a mining face in the Inner Mongolia Autonomous Region.The analysis results demonstrate that this model achieves a reduction in MAE of 20.81％and 30.17％respectively,a decrease in RMSE of 0.063 and 0.142 respectively,and an improvement in R2 of 0.023 and 0.075 respectively,compared to the single models based on the Long Short-Term Memory Neural Network(LSTM)and Bi-directional Long Short-Term Memory(BiLSTM)neural network.These findings provide evidence that this model exhibits superior accuracy,generalization,and robustness in the presence of complex factors.
safety engineeringgas emissiongrey wolf optimizationbi-directional long short-term memoryprincipal components analysis
万方数据
维普
