A prediction model of mining subsidence based on an unskewed continuous probability distribution over an infinite interval

Mining subsidence is a serious threat to the ecosystems of mining regions. Accurate prediction of mining subsidence is essential for a scientific assessment of mining-induced damage. This study proposed a prediction model of mining subsidence based on an unskewed continuous probability distribution over an infinite interval. This method is an extension of the commonly used subsidence prediction method, the probability integration method, employed by Chinese engineers. It has the same scope of application as the probability integration method. This method does not have a specific expression and encourages engineers to screen appropriate unskewed continuous probability distribution functions and incorporate them into this model to establish prediction equations. By not solely considering one prediction method developed based on a specific function, it avoids limiting the improvement of prediction accuracy. The reliability of this method was validated by comparing it with actual subsidence data from the mining area. The results indicate that this approach can further improve the accuracy of mining subsidence prediction compared to the probability integration method.