Abstract: In order to effectively determine the prediction parameters of probability-integral method and to improve the prediction accuracy, a new method by combining Particle Swarm Optimization (PSO) algorithm and BP neural network (PSO-BP) was presented. In this method, an improved hybrid PSO algorithm was used to optimize the connection weights and thresholds values of BP neural network. An optimization model for prediction parameters of probability-integral method using this hybrid PSO-BP neural network algorithm was constructed based on analyzing the relationship between the parameters and geological mining conditions. Typical data of surface moving observation stations was used as learning and test samples. Analysis was made by comparing calculated values, observed values, and values of improved BP neural network. Results indicate that PSO-BP calculation model has higher convergence speed and higher precision.

Key words: surface movement, probability-integral method, particle swarm optimization algorithm, BP neural network, optimal selection