Coal mining face gas concentration prediction method based on stacked LSTM
With the development of coal mining in the Huainan mining area towards deep coal seams,the issue of coal and gas outburst has become an important factor restricting the production capacity of the Huainan mining area.In order to improve the accuracy of gas concentration prediction methods,a stacked LSTM model structure is proposed based on the Long Short Term Time Memory Network(LSTM),imitating the deep neural network to enhance feature capabilities through multi-layer stac-king.By deeply probing into the correlations in gas concentration time series data through multi-layer LSTM structure,the ac-curacy of gas concentration prediction can be improved.Using gas monitoring data from Zhujidong Coal Mine in Huainan as a sample,a dataset was created using the sliding window method,and a corresponding experimental platform was built for train-ing and verification.The experimental results show that using stacked LSTM structure can reduce the average absolute percent-age error of network structure compared to traditional LSTM,and more accurately predict coal mine gas concentration.
万方数据
维普
