###### Abstract  
The accurate inversion of mining subsidence prediction parameters is key to the precise prediction of deformation during mining. However, the use of traditional genetic algorithms (GA) for inversion prediction has problems such as poor resistance to differences, and the accuracy of inversion parameters is affected when key monitoring points are missing. In response to these issues, a probability integral parameter inversion method is proposed in this study that integrates a selection-weighted iterative robust genetic  
using surface measurement data is affected by the model errors of the probability integration method itself, resulting in poor stability and reliability. Therefore, further research of current parameter inversion methods is still needed. Research shows that there are two types of methods commonly used in parameter prediction. The first is a parameter prediction method based on artificial intelligence (AI) [11; 12; 13; 14; 15; 16; 17], which uses a large number of measured prediction parameters and coal mining parameters as the training sets, and the parameters of the PIM are obtained through various artificial intelligence prediction models. The second method obtains probability integral parameters based on intelligent optimization algorithms [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. This method first uses the PIM to establish the error equation between the cost function and the parameters of the PIM. Then, an intelligent optimization algorithm is used to perform the optimal estimation of the PIM parameters, thereby obtaining the optimal estimation value of the mining subsidence prediction parameters. It is difficult to achieve the optimal prediction of all PIM parameters using the AI-based method due to limitations in AI development and the complexity of mining subsidence problems. The PIM-based parameter acquisition method based on intelligent optimization algorithms can estimate all parameters optimally, but different intelligent optimization algorithms have different advantages and disadvantages. For example, the selection of initial parameters has a significant impact on the vector model method, and unfavorable initial values may fall into local optimal traps. GAs are susceptible to observation errors, which can inaccurately estimate PIM parameters. The above analysis shows that, between the two types of mining subsidence parameter inversion methods, the PIM parameter acquisition method based on intelligent optimization algorithms has certain advantages, but the selection of initial parameter values and the impact of observation value errors require further research. Following an analysis of the literature, a PIM parameter inversion method is proposed in this study that integrates the selection-weighted iterative robust genetic algorithm (the weight matrix of different observations is updated by the IGG (Institute of Geodesy and Geophysics) weight function and the residuals of the observations, and the weights of the observations containing the error are reduced to achieve immunity). The method is as follows: first, based on the basic principle of obtaining PIM parameters based on an intelligent optimization algorithm, the error equation of the parameters is established. Second, the IGG weighting iteration function (the weight function of a robust estimation method) method is used [30; 31; 32], and a parameter cost equation based on the IGG method is constructed (increasing the influence of observation error on the parameter estimation accuracy). Finally, the parameter error equation is solved using the constructed parameter cost equation based on the IGG method to obtain the optimal PIM parameter value. The reliability and feasibility of the proposed method are proven using simulation experiments and specific engineering examples. Based on the simulation experiments, the influence of k value selection in the IGG method's weight iteration function and the different survey line layout forms on the inversion probability integral parameters is discussed in detail.