Abstract
For the problem of low prediction accuracy caused by traditional neural network gas concentration prediction models which did not consider temporal and spatial characteristics of gas data, this paper proposed a Spatial-Temporal graph neural network gas prediction model based on Spatial-Temporal data. Its essence was the integration of graph convolutional network and WaveNet network. In spatial dimension, graph convolutional network was used to aggregate the information of neighbor nodes, and adaptively adjusts the spatial association strength of each node according to the attention mechanism to captured the spatial characteristics of gas data. In temporal dimension, WaveNet network model was introduced, Dilated Causal Convolution was used to extract the temporal characteristics of gas data on temporal dimensions. According to the distance between gas sensors in the mine, the gas data spatial structure was constructed by Thresholded Gaussian kernel function. Experiment with the measured gas temporal and spatial data, using Mean Absolute Error (MAE) as an indicator of predictive accuracy. The experimental results show that the prediction model mentioned in this paper is significantly improved compared with the prediction accuracy of other predictive models.
Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine
