## 2 Working Resistance Prediction Model for Supports in Ultra-Long Mining Faces
### Support Pressure-Prediction Model  
#### 2.2.1 Modeling Process  
With the cyclic lowering and uplifting of supports during the advancement of a fully-mechanized mining face, the load of the overlying strata on the mining face will vary in different regions and between different supports along the dip direction. The resistance variation of the target support and adjacent supports is similar. Hence, to predict the support resistance of the mining face, the AGCRN model can be employed to obtain the dynamic correlation between the resistances of the supports based on the NAPL module. The overall flow of the model is presented in Figure 3 and described below.  
(1) Dataset construction. The dataset utilized was the end-of-loop resistance data of the support during the mining of the #202 mining face of a coal mine in Shaanxi from July to December 2021. The mining height of the working face is 1.8-2.6 m. The data frequency was the mining cycle/time. The end-of-loop resistance of the support reflects the maximum load during each mining cycle and can characterize the bearing capacity of the support and the breakage of the roof.  
(2) Data preprocessing. Abnormalities such as data loss, outliers, and noise are often encountered in the monitoring, analysis, and data transmission processes. To ensure data quality, it was necessary to preprocess the raw data. Outliers occurred during circumstances in which there were short or long intervals of data collection. In this study, the outliers of the support working resistance with a duration of less than 20 min or longer than 3 h were eliminated. The missing values of the support working resistance are generally due to data loss during transmission or sensor errors. For the missing values, the Lagrange interpolation method was applied to fill the gaps.  
(3) Time dimension unification. The dataset contained multiple time series and was converted into a graph structure. The amount of original data was huge and heterogeneous, and the monitoring time points for the different supports were different. It is very difficult to analyze the spatial relationship without a unified time scale. To unify the time dimension of the data, the end-of-loop resistance data for the day were averaged and employed as  
Figure 3: Flow chart of roof-weighting prediction.  
Figure 2: Structure of the AGCRN model.  
the data for the day in the extraction process. The extraction process of support resistance features is shown in Figure 4.  
(4) Data normalization. Normalization is generally used to adjust the interval of the data proportionally, such that the data are converted into the same value range. Data normalization can effectively reduce the impact of excessive eigenvalues in the original data and can reduce training errors. In this study, the min-max method was applied to normalize the original data to the range of [0,1]. The formula is as follows:  
\[\overline{x}=\frac{x-min(x)}{max(x)-min(x)} \tag{11}\]  
where \(x\) is the original resistance data; \(\overline{x}\) is the normalized resistance data; and max and min are the maximum and minimum values in the original resistance data, respectively.  
(5) Training set and testing set. It is necessary to divide the dataset into a training set and a testing set. During the construction of the model, it is also essential to check the degree of fitting and to select the optimal hyperparameters. Hence, the training data were further divided into a training set and a verification set. After training, it was necessary to objectively evaluate the generalization ability of the model, and the verification set was used as the testing set. The original resistance data were divided according to a ratio of 7:3.  
(6) Training and verification. The training set was utilized to optimize the model and determine the best hyperparameters, and the testing set was employed to conduct the prediction. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as the performance indices to evaluate the model's accuracy.