# Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches
## 2 Materials and Methods
### Method for Predicting Support Resistance Data  
This is the first step in predicting pressure exertion in the working face, which involves forecasting the support resistance data for advanced cycles. This is a crucial aspect of data-driven pressure exertion prediction in the working face and significantly affects the accuracy of the prediction. It is currently one of the most extensively researched areas. Support resistance is correlated with various modes of input data, including historical support resistance data, data reflecting overburden activity, and data reflecting the geological conditions of the mining face. By inputting these types of data, predictions of support resistance data can be achieved. Current research on support resistance prediction mainly focuses on predictions based on historical support resistance data, and this paper also only considers prediction results based on historical support resistance data.  
For a long time, support resistance prediction based on historical information has been grounded in the temporal correlation of support resistance data, utilizing deep time series models represented by LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), as evidenced in the research by [PERSON] [23], [PERSON] [24], [PERSON] [25], and others. In recent years, researchers have discovered that the spatial correlation of support resistance data is crucial for prediction results, and they have attempted to improve prediction performance by extracting spatial correlation while modeling the temporal correlation of support resistance data. [PERSON] [26], [PERSON] [27], and others have explored methods that combine machine learning algorithms to extract spatial correlation with deep time series models to extract temporal correlation. However, deep learning has been proven to outperform machine learning, which is a limitation of this approach. [PERSON] et al. [28] employed an adaptive graph convolutional neural network to extract the spatiotemporal relationship of support resistance data, resulting in a significant reduction in prediction error. Based on these studies, we propose a support resistance prediction method based on a deep spatiotemporal sequence model. This model uses a convolutional neural network to extract spatial features, which excels in processing grid data with spatial correlation, such as images [29; 30; 31], and a recurrent neural network to extract temporal features, which is adept at handling sequence data with temporal dynamic information, such as text [32]. By integrating the two, the deep spatiotemporal sequence model addresses the challenge of unified modeling of spatiotemporal features, making it highly suitable for support resistance prediction. Experiments have demonstrated its advantages in this context [33]. In the first stage of support resistance prediction, we will select the PredRNN(Predictive RNN) deep spatiotemporal sequence model, which achieves the best experimental results. The detailed structure of the PredRNN model will not be repeated here and can be found in the relevant literature [33; 34].