Graph Convolutional Network-Guided Mine Gas Concentration Predictor

Abstract

Coal mining work has always been a high-risk job, although mining technology is now regularly very mature, many accidents still occur every year in various countries around the world, most of which are due to gas explosions, poisoning, asphyxiation and other accidents. Therefore it is important to monitor and predict both underground mine air quality. In this paper, we use the GCN spatio-temporal graph convolution method based on spectral domain for multivariate time series prediction of underground mine air environment. The correlation of these sequences is learned by a self-attentive mechanism, without a priori graph, and the adjacency matrix with an attention mechanism is created dynamically. The temporal and spatial features are learned by graph Fourier transform and inverse Fourier transform in TC module (temporal convolution) and GC module (graph convolution), respectively. Besides, the corresponding experimental predictions are performed on other public datasets. And a new loss function is designed based on the idea of residuals, which greatly improves the prediction accuracy. In addition, the corresponding experimental predictions were performed on other public datasets. The results show that this model has outstanding prediction ability and high prediction accuracy on most time-series prediction data sets. Through experimental verification, this model has high prediction accuracy for dealing with multivariate time series prediction problems, both for long-term and short-term prediction.