A Graph Convolutional Encoder-Decoder Model for Methane Concentration Forecasting in Coal Mines

Abstract

Methane is one of the most dangerous gases produced in the process of coal mining. Because of its flammable and explosive characteristics, it has seriously threatened the life and property safety of coal miners. As a result, accurate and real-time gas concentration forecasting is becoming a crucial but challenging issue for reducing methane risks and accidents. To further improve the efficiency and accuracy of methane concentration forecasting, this paper proposes a graph convolutional encoder-decoder (GCN-ED) network, which can train and infer all the sensors of a coal face as a unified entity. The proposed GCN-ED is composed of the GCN module and the ED module with a parallel structure. The GCN module constructs a priori graph structure through the adjacency relation between sensors in reality and uses a learnable self-adaptive dependency matrix to precisely capture the hidden spatial dependency in the data. The ED module is used to learn complex temporal features with LSTM cells and generate multi-step results of the gas concentration prediction. Experiments are conducted on real coal mine datasets, whose results demonstrate that the GCN-ED achieves the better performance than various state-of-the-art solutions and largely improves the efficiencies of training processes.

Keywords

• Graph convolution
• encoder-decoder model
• spatio-temporal data
• methane concentration forecasting
• coal mine safety