## 4 Conclusions  
(1) Based on working resistance data for supports recorded during the mining of a fully mechanized mining face in the Yushen mining area in northern Shaanxi, the NAPL module and DAGG module in the AGCRN model were used to dynamically extract the spatiotemporal correlation between the resistance data for adjacent supports in the mining face. An AGCRN prediction model was developed to predict the working resistance of the supports in fully mechanized mining faces.  
(2) The MAE and MAPE were employed as performance evaluation indices. When the node-embedding dimension was set to 10 and the time window was set to 16, the corresponding MAE and MAPE values of the prediction model were the minimum values.  
(3) Three reference models (i.e., the BP, GRU, and DCRNN models) were selected and compared with the proposed AGCRN model. The results reveal that compared with the reference models, the MAE and MAPE of the AGCRN model were 38.75% and 23.49% lower, respectively, indicating that the AGCRN model effectively captures spatiotemporal correlation information and demonstrates high accuracy in predicting the working resistance of supports.  
(4) The AGCRN model was applied to the prediction of roof weighting in an ultra-long fully mechanized mining face in the Yushen mining area in northern Shaanxi. The results show that the working resistance of the supports in the lower and upper areas was relatively small along the strike, whereas the working resistance of the supports in the middle area was large, exhibiting a zoning pattern of \"low-high-low\" in terms of the average working resistance. During the periodic roof weighting, the working resistance curve of the supports in the middle area displayed a double-peak pattern. The overall working resistance of the fully mechanized mining face had a low-high-medium-high-low trend along the strike. We compared the predicted results with the field-measured data and related literature and found that the predicted model has good accuracy. In the next  
Figure 10: Working resistance distribution in different areas under the condition of roof weighting, they should be listed as: (**a**) Initial roof weighting; (**b**) First-cycle roof weighting.  
step, we will combine the numerical analysis results and continuously optimize the model parameters to expand the application range and effect of the model.  
Conceptualization, X.G., Y.H. and J.Y.; methodology, Y.H. and K.F.; software, S.L. and L.Y.; validation, J.Y., X.G. and K.F.; formal analysis, X.G. and L.Y.; investigation, L.Y.; resources, K.F.; data curation, S.L., K.F. and L.Y.; writing--original draft preparation, X.G., Y.H. and J.Y.; Writing-review and editing, X.G. and K.F.; visualization, S.L.; supervision, K.F.; project administration, L.Y.; funding acquisition, K.F. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Basic Research Program of Shaanxi (Program No. 2021 JLM-10), Shaanxi Provincial Outstanding Youth Science Foundation Project (2023-JC-JQ-42), The Youth Innovation Team of Shaanxi Universities. The APC was funded by the Natural Science Basic Research Program of Shaanxi (Program No. 2021 JLM-10).  
Not applicable.  
The data used to support the findings of this study are included in the article.  
The authors declare no conflict of interest.