# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 1 Introduction  
Mining-induced surface deformation is a worldwide issue [1; 2; 3; 4]. After coal mining, the initial stress state of rock in the goat will be destroyed, and the overlying strata will reach a new equilibrium state through bending, caving, spalling and other forms. In this process, the surface will deform and eventually form collapse pits, which will affect surface water circulation and destroy the surface ecological environment of the mining area [5; 6; 7; 8]. Therefore, accurately predicting surface movement and deformation is meaningful to the exploitation of coal resources and can support rational relocation, restoration and rebuilding of the ecological environment in mining areas [9; 10]. At present, the prediction methods of mining-induced surface deformation mainly include the empirical method, the theoretical model method and the influence function method based on measured data [11; 12; 13; 14]. Among them, the PIM has high prediction accuracy and simple prediction degree, and has been widely used in China [15; 16; 17]. According to the literature [18; 19; 20], the prediction error of the PIM mainly comes from the model error and calculation parameter error. Model error mainly comes from the disparity between assumptions derived by the PIM and complex geological and mining conditions, and it is often difficult to improve. Therefore, an appropriate method is used to determine the uncertainties in the model parameters of the deformation, which are critical. The prediction errors of the PIM mainly come from model errors and parameter errors. Model errors mainly come from the inconsistency between the assumptions of the PIM and the actual complex geological mining conditions, which are often difficult to improve. Therefore, reducing parameter errors of PIM is an important means to improve the prediction accuracy. The model parameters of PIM can be obtained by data-based methods and analogy methods [21; 22]. The former use the periodic monitoring data of surface deformation to invert the surface prediction parameters and predict the surface deformation. These methods are of high accuracy and reliability. [PERSON] [23] proposed the least squares method based on the measured data to obtain the parameters of the predicted model through the linearization of the vertical deformation. [PERSON] et al. [24] used the improved fireworks algorithm to invert deformation parameters for the observation data of a mine in Huainan over 20 months. However, the measured parameters have a lag in guiding the prediction of the observed working face. Generally, the observation time is long (2-3 years), which needs substantial energy to construct and maintain the monitoring station, and it is impossible to effectively guide the new mining face. The analogy methods are to select statistical analysis results of parameters to directly determine parameters. The most common methods are the statistical model method and the neural network method. [PERSON] [25] used orthogonal analysis to obtain the horizontal movement value and verified it. [PERSON] et al. [26] used the measured data to comprehensively study the methods of analogy theory and fuzzy recognition theory, and a comprehensive analogy method for surface prediction parameters suitable for the Chongqing mining area was finally proposed. [PERSON] [27] obtained the estimated surface parameter values based on the measured deformation values and geological and mining conditions. The essence of the statistical model is a more intuitive empirical model, simple and intuitive. It is commonly used to summarize the characteristics of data rules. However, it considers fewer influencing factors. In some cases, it cannot obtain satisfactory fitting results, and it lacks generalization ability [28]. As for the neural network method, although it has high accuracy, it often requires more monitoring data, and the model is prone to falling into local minima, affecting fitting accuracy [29].