Knowledge-based and data-driven underground pressure forecasting based on graph structure learning.

The pressure prediction technology whereby represents the rock pressure law in the excavation is fundamental to safety in production and industrial intelligentization. A growing number of researchers dedicate that machine learning is used to accurate prediction of underground pressure changes. However, the existing research which based on the classical machine learning rarely considers the cause between inducement of underground pressure and the underground pressure change. In this paper, we propose a novel Reinforced and Causal Graph Neural Network, namely RC-GNN, for the prediction task, to overcome the shortage of causal logic. First, we build a causal graph by considering internal relations between inducement and display of pressure and employ prior knowledge to erect the early and properties of the graph. Second, we construct the prediction network for underground pressure by graph convolutional networks and long short-term memory. Finally, we use the performance index of underground pressure prediction to design a reinforcement learning algorithm, which achieves optimization of the causal graph. Compared to six representative methods, experimental results with 18-60% increases in performance on the real prediction task.