Search

In view of the probability integration method, mining subsidence prediction parameter inversion is a highly nonlinear problem. The algorithm is unstable during inversion, and it is easy to fall into a local optimal solution. It can be used to solve nonlinear problems, has strong robustness, and has a better overall situation. The artificial fish school algorithm (AFSA) with optimizing ability is introduced into the inversion of prediction parameters of mining subsidence using probability integral method. This algorithm has been applied in the optimal allocation of water resources in Guangdong Province and the optimization of distribution routes. Scholars have not yet seen the application of AFSA to the inversion of probabilistic integral model parameters. In view of this, this paper introduces AFSA into the inversion of probabilistic integral model parameters for the first time, and constructs an inversion method of probabilistic integral mining subsidence prediction parameters based on AFSA. Research indicates that the AFSA probabilistic integral model parameter inversion method is applied to the ground movement measured data of a mine in Huainan, and the predicted parameters of the probability integral method are as follows; $=1.059 2, tan£=2.020 3, 6=0.404 9, 5=87.220 9 °, Sj =1.284 0 m, S2 =0.453 0 m, S3 = 62.200 0 m, S4 =44.753 1 m. The error in fitting of sinking and horizontal movement is 131.74 mm, which meets the accuracy requirements of engineering applications. [ABSTRACT FROM AUTHOR]