## 1 Introduction  
The exploitation of coal resources can cause various environmental and geological disasters [1; 2], which mainly refer to surface subsidence. Accurate prediction of surface subsidence is an important means to prevent mining damages and reduce subsidence disasters. Meanwhile, it is directly related to the reuse of the area of land resources. Therefore, the researchers have conducted exhaustive research on the prediction methods of surface subsidence and have achieved fruitful results.  
In the mining surface subsidence prediction of the caving mining, [PERSON] adopted the profile function method to conduct surface subsidence prediction of the inclined coal seams [3]. [PERSON] established a dynamic prediction method of surface subsidence that incorporated the time factor [4]. A three-dimensional n-k-g influence function was used by [PERSON] to predict the surface subsidence [5]. Moreover, [PERSON] employed the spatial statistical method to learn the surface subsidence prediction method [6]. The probability integral method was proposed by [PERSON] to further perfect [PERSON]'s random medium theory [7]. [PERSON] also established the Weibull method of surface movement, with the overlying strata as a tatter block [8]. In addition, some scholars also worked on the principles of surface subsidence such as insufficient mining [9; 10], deep mining [11; 12; 13; 14], thick soil mining [15; 16] and other special mining conditions [17; 18; 19; 20], with the corresponding prediction approaches of surface subsidence proposed. For the prediction of surface subsidence in backfilling mining, [PERSON] put forward the surface subsidence prediction model based on the equivalent mining height theory [21]. The prediction models established above can solve practical issues to a certain extent. However, the probability integral method is the most widely used surface subsidence prediction method.  
The prediction parameters of the probability integral approach include subsidence coefficient, horizontal movement coefficient, tangent of major influence angle, mining influence propagation angle and inflection point offset. The five parameters intuitively reflect the relationship between the surface subsidence basin and the area. Through the inversion of measured data, the prediction parameters of most mines in China have been obtained, enabling the probability integral method the most widely applied method. However, the prediction precision of this method largely depends on the accuracy of the selected parameters. When the mining area absences measured data or the geological and mining conditions vary, particularly for large-scale surface subsidence prediction, it will cause the lack of a systematic summary for properly selecting prediction parameters. Therefore, it is necessary to study the influence law of geological and mining conditions on the prediction parameters of the probability integral means. The prediction parameters can be selected according to the relationship between the parameters and the geological and mining conditions. Although some scholars have worked on the influence of geological and mining conditions on the prediction parameters [22; 23; 24; 25], a more systematic analysis and summary still are required. Consequently, this paper systematically investigates the impact of geological and mining conditions on the prediction parameters of the probability integral method based on the theoretical analysis, and the corresponding research results were attained. The findings can offer a scientific basis for properly choosing prediction parameters of the probability integral method. Eventually, the research outcomes of this paper can be used to predict the surface subsidence of northern Pei County and lay the foundation for the integration of this area.