Intelligent prediction for support resistance in working faces of coal mine: A research based on deep spatiotemporal sequence models.

Accurately predicting support resistance in coal mine working faces is a pressing matter in the field of intelligent mining research, which holds immense importance in enhancing the safety of coal production and the overall level of intelligent mining. The current research in this field primarily focuses on utilizing deep temporal sequence models for prediction. While there have been promising advancements in this area, concerns persist regarding the dependability of predicted outcomes. In fact, the movement of overburden in mining faces varies over time and space, and the changes in the support resistance that reflect the instability of overburden movement also have temporal and spatial characteristics. Therefore, in order to improve the reliability of model prediction, this paper proposes the application of deep spatiotemporal sequence models for support resistance prediction, and identifies the PredRNN and PredRNN++ models as suitable options. The exploration on the application of deep spatiotemporal sequence models for predicting the support resistance has been conducted. The proposed application involves the establishment of a fully working face area grid model with support number and mining cycle as coordinate units. Different from deep temporal sequence models that take the studied grid features as observations, the 25 adjacent spatiotemporal feature grids corresponding to the studied grid are considered as observations for the deep spatiotemporal sequence models. Based on the predicted three-dimensional tensor, the required predicted support resistance data can be obtained. Based on the complete support resistance data from 9 working faces in a coal mine in Datong mining area from 2009 to 2018, a comprehensive dataset was established to experimentally analyze the predictive performance and reliability of the LSTM, PredRNN, and PredRNN++ models. The experimental results demonstrate significant improvements in predictive performance and reliability of deep spatiotemporal sequence models compared to the current mainstream deep temporal sequence models. In terms of predictive performance, the PredRNN and PredRNN++ models have improved their feature extraction ability by incorporating the convolution operations to extract the spatial correlation of the input data. Furthermore, through the incorporation of gated units and nonlinear neurons into the storage units, coupled with the integration of vertical memory state transitions, the depth of the network has significantly increased, resulting in the enhancement of the sequence modeling capabilities. In terms of reliability of prediction results: The acceptable threshold for prediction error was set at 1000 kN. According to the average prediction results of all samples in the test set, it was found that the deep temporal sequence model was unable to accurately predict support resistance ahead. While the deep spatiotemporal sequence model demonstrated a predictable sequence length of 8. According to the prediction results of all samples in the test set, the accurate prediction rate increased from 30 %∼35 % to 65 %∼85 % and the maximum MAE value decreased from 10000 ∼ 12500kN to 6500 ∼ 9000kN in more than 20,000 test samples. The PredRNN model with an input sequence length of 50 achieves an accurate prediction rate of 85.48 % and a maximum MAE value of 6698kN, making it the most reliable prediction model. [ABSTRACT FROM AUTHOR]