# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 7 Conclusions  
The selection of parameters of subsidence prediction lacks theoretical basis in mining areas without measured data or with special geological and mining conditions. In this paper, the Huainan mining area was chosen as the object of research. The mechanism of the influence of geological and mining conditions on parameters of PIM was deeply analyzed. Then, a combined prediction model was constructed by integrating the parameters of the multiple regression model and the extreme learning machine with the parameters of PIM. The measured surface subsidence values in mining areas were taken as the learning, training, and testing samples of the network to verify the accuracy and reliability of the model. The effects of the number of nodes in the hidden layer, the selection of activation function, and the ratio of training set to test set on the results were illustrated. It provides an effective way to select related parameters of the surface movement basin of new working faces.  
It needs to be explained that the verification of the CM-GA-ELM model in this paper is mainly aimed at the Huainan mining area, so the results are better applicable to this mining area; further verification is still needed for other thick-, loose-layered mining areas. The gaps between the rocks left after coal mining will be gradually compacted by the overlying rocks, and the surface will slowly deform during the compaction process. The probability integral method model assumes that the overlying rocks have been compacted. The parameters were obtained under compaction conditions, but with the passage of time  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Number of Test Samples**} \\ \cline{2-6}  & **14** & **21** & **28** & **35** \\ \hline \hline Subsidence & MeaRE & 5.509 & 5.588 & 6.236 & 7.824 \\ coefficient & RMSE & 0.050 & 0.051 & 0.059 & 0.072 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction results (unit:mm)they will still cause a slight subsidence of the surface. and we will carry out related research in the future.  
Conceptualization, S.C. and X.Y.; methodology, L.W.; validation, S.C., X.Y. and L.W.; formal analysis, S.C.; investigation, S.C.; resources, X.Y.; data curation, X.Y.; writing--original draft preparation, S.C.; writing--review and editing, S.C.; visualization, L.W.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Anhui Colleges (Grant NO.KJ2021A0422), the Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring (Grant NO.KSXTJC20201), the Introduction of Talent Research Start-up Fund of Anhui University of Science and Technology (Grant No. 2021 yjrc43), the Major Science and Technology Projects of Anhui Province (Grant NO. 202103a05020026), the Key Research and Development Program of Anhui Province (Grant NO. 202104a07020014), and the Natural Science Foundation of China (Grant No. 52074010).  
Not applicable.  
The data used in this article is not public.  
We gratefully acknowledge many important contributions from the authors of all the reports cited in our paper.  
The authors declare no conflict of interest.