Abstract:Slope stability is affected by a variety of complex factors, and it is difficult for traditional algorithms to obtain high precision prediction results. In order to timely and accurately analyze the reliability of slope stability, an improved particle swarm optimization extreme learning machine (IPSO-ELM) model was proposed and applied to the example of slope stability prediction.First in the basic particle swarm optimization (PSO), on the basis of which were liable to occur during the optimization process in order to overcome the local optimal problem, the introduction of the adaptive weight method, the improved particle swarm optimization (IPSO) for extreme learning machine (ELM) the weights of the input and hidden layer offset optimization, significantly improve the generalization ability and forecasting precision of the ELM model, and then will IPSO - ELM model, the PSO - ELM model and the predicted values and the real value of the ELM model comparison and analysis, the results show that IPSO - ELM model prediction approach in the real value, have higher prediction accuracy,The feasibility and effectiveness of IPSO-ELM model in slope stability evaluation are verified.