A Graph Neural Network Gas Concentration Prediction Model Based On Spatial-temporal Data

Gas outburst is one of the five major disasters in coal mines.Timely,accurate and effective prediction of gas concentration is the key to prevent gas concentration disasters.Neural networks have been widely used in the field of gas concentration prediction due to their strong self-learning ability and robustness.However,most of the traditional neural network prediction models do not consider the spatiotemporal characteristics of gas concentration data,and only use the single-point time series prediction method to predict the gas concentration,which affects the accuracy of the gas concentration prediction at the collecting face.In this paper,by analyzing the spatiotemporal characteristics of gas concentration data,a spatiotemporal graph neural network gas concentration prediction model based on spatiotemporal data is constructed——AGCN-WaveNet.The main research contents of this paper are as follows:First,to address the problem that most existing spatiotemporal graph network models arc less effective in capturing the long-range temporal sequences of spatiotemporal data,this paper uses graph convolutional network combined with WaveNet model to construct spatiotemporal graph network models.In the spatial dimension,use graph convolutional network to aggregate the information of neighbor nodes.In the temporal dimension,the WaveNet network is introduced to extract the time characteristics of data.By using the dilated causal convolution module of the WaveNet model,the spatiotemporal graph network model can be used to obtain very large receptive field with a small number of network layers.It also effectivcly avoids the problems such as gradient explosion.Secondly,when modeling for most spatiotemporal network,it is assumed that the spatial relationship between nodes is fixed.However,the fixed spatial structure does not necessarily represent the spatial relationship between nodes completely,which may lead to the lack of spatial relationship and reduce the performance of the model.In this paper,the attention mechanism is introduced to realize the dynamic adjustment of spatial correlation strength between nodes,so that the model can more completely represent the spatial relationship between nodes.The comparison of experimental performance under public datasets shows that the introduction of attention mechanism improves the prediction accuracy of the model.Finally,in view of the low prediction accuracy of the traditional single-point gas concentration prediction model,this paper uses the gas concentration prediction model based on the AGCN-WaveNet model to apply it to the measured gas concentration data set.The spatial structure of the gas concentration data is constructed using an improved Gaussian function based on the wind direction and the distance between gas concentration monitoring points in the mine.The experimental data were collected from a number of gas concentration monitoring points at the working face of a mine.A comparison of the model prediction results with existing gas concentration prediction models shows that the model proposed in this paper can better fit the changing trend of gas concentration and improve the prediction accuracy as it also takes into account the spatial and temporal characteristics of gas concentration data.Based on the AGCN-WaveNet gas prediction model,a simple gas concentration prediction system is designed using C#.The system has a simple interface,is easy to operate and can make accurate forecasts of gas content at the working face,which can effectively prevent the occurrence of gas disasters in mines.