哈尔滨商业大学学报（自然科学版）2024，Vol.40Issue(2)：193-199,7.
基于SG-LSTM-GRU井下粉尘体积分数预测模型
Prediction of underground dust concentration based on SG-LSTM-GRU model
摘要
Abstract
Dust concentration monitoring plays an important role in coal mine dust warning,and predicting its concentration changes is beneficial for ensuring underground safety production and reducing occupational disease risks for miners.A SG-LSTM-GRU prediction model had been established for the prediction of underground dust concentration.Preprocess the monitoring data by first using the SG filtering method to reduce the presence of noise in the time series data.Used the maximum and minimum value method for normalization.Divide the obtained dataset into a 90%training set and a 10%testing set.Continuously experiment through enumeration to determine the parameter values in the SG filter and the time step size in the model.Combined LSTM and GRU models.Both the LSTM layer could retain more features in the data,and the GRU layer could improve the training speed of the model.Root mean square error,mean absolute error,and calculation time were used for model comparison and evaluation.The research results indicated that the RMSE and MAE predicted by the SG-LSTM-GRU model were 0.256 and 0.066,which was better than other prediction models.Therefore,adopting the SG-LSTM-GRU model for predicting underground dust concentration could improve the accuracy of dust concentration data prediction,achieve safe production in coal mines underground,and reduce the risk of pneumoconiosis among miners.关键词
机器学习/LSTM/GRU/Savitzky Golay滤波器/粉尘体积分数预测/安全生产Key words
machine learning/LSTM/GRU/Savitzky Golay filter/dust concentration prediction/safe production分类
矿业与冶金引用本文复制引用
牛莉莉,杨超宇..基于SG-LSTM-GRU井下粉尘体积分数预测模型[J].哈尔滨商业大学学报（自然科学版）,2024,40(2):193-199,7.基金项目
国家自然科学基金项目(61873004):"多源传感器环境下基于异物特征信息融合的行为识别". （61873004）
