Steady Mining Subsidence Prediction Method by Fusing Multi-source Parameters Identification Results

In order to overcome the problem of unreliability and inaccuracy results of the single parameter identification model leading to the unsteady of mining subsidence prediction results, steady mining subsidence prediction method by fusing multi-source parameter identification model is studied in this paper, a new model for mining subsidence prediction is established by integrating the parameters identification results of model vector method, genetic algorithm and simulated annealing based on the least square principle, and the feasibility of the model is simulated. The experimental results show that the subsidence error and horizontal displacement error of the new prediction model is better than the estimated error of parameters inversion based on single mode vector method, genetic algorithm, simulated annealing method; new prediction method by fusing multi-source mining subsidence prediction parameters identification model has a certain anti-error ability.