PSO-ELM prediction model for surface deformation of mine slope

In order to improve the model accuracy of slope surface deformation prediction data, the influence factors of surface deformation of mine slope was considered and the prediction model of the limit learning machine was established based on particle swarm optimization. Firstly, the mine slope surface deformation monitoring data and influencing factors data were used to establish the prediction model utilizing the classical particle swarm optimization algorithm and the limit learning machine method. Secondly, the surface deformation of the mine slope and its influencing factors were collected in Anjialing open-pit mining area. Particle swarm optimization(PSO) was applied to optimize the connection weight and threshold of the input layer and the hidden layer to improve the prediction accuracy of the model. Finally, through the optimization application of PSO, the maximum relative error(4.705×10^-8), mean square error(6.243×10^-5) and root-mean-square error(0.008) of the prediction model were reduced to 1.516×10^-8, 1.158×10^-5 and 0.003 respectively. The experimental results showed that the proposed prediction model had higher prediction accuracy than other models, and it could be applied to the prediction of surface deformation of mine slope in the follow-up study, so as to improve the safety level of mine.