Abstract: The slope displacement is an explicit process of the complicated dynamic system involving many mutual facts. And the physical modeling is very difficult to fulfill prediction function. As an alternative, it is proved by many study work in which a set of displacement time series to predict the future displacement can be used; and united many different methods as a new predicting model was a principal trend of study. Based on this, a novel model based on wavelet transform and genetic¬ algorithm -least square support vector machine (GALSSVM) for slope nonlinear displacement forcasting is proposed. Firstly, slope displacement time series are decomposed into different frequency signals through the wavelet transform. Secondly, phase space of each signals is reconstructed, and time delay and embedding dimension are determined by mutual information method and false nearest neighbor method respectively.Then, the respective forcasting model of genetic¬-least square support vector machine is constructed according to different characteristics of each phase space. Lastly, the predicted results of the signals are reconstructed to be used as the final prediction result of slope displacement. As a test, this model has been used in displacement prediction of DANBA slope. The results indicate that it is reliable with high precision; and it can be used to practical engineering

Key words: slope, time series, wavelet transform, genetic algorithm-least square support vector machine, phase space, displacement, prediction