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Predicting landslide displacement is\udchallenging, but accurate predictions can prevent casualties and economic\udlosses. Many factors can affect the deformation of a landslide, including the\udgeological conditions, rainfall and reservoir water level. Time series\udanalysis was used to decompose the cumulative displacement of landslide into\uda trend component and a periodic component. Then the least-squares support\udvector machine (LSSVM) model and genetic algorithm (GA) were used to predict\udlandslide displacement, and we selected a representative landslide with\udepisodic movement deformation as a case study. The trend component\uddisplacement, which is associated with the geological conditions, was\udpredicted using a polynomial function, and the periodic component\uddisplacement which is associated with external environmental factors, was\udpredicted using the GA-LSSVM model. Furthermore, based on a comparison of the\udresults of the GA-LSSVM model and those of other models, the GA-LSSVM model\udwas superior to other models in predicting landslide displacement, with the\udsmallest root mean square error (RMSE) of 62.4146 mm, mean absolute error\ud(MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of\ud1.492 % at monitoring station ZG85, while these three values are\ud87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm,\ud48.5392 mm and 3.131 % at ZG87. The results of the case study suggest\udthat the model can provide good consistency between measured displacement and\udpredicted displacement, and periodic displacement exhibited good agreement\udwith trends in the major influencing factors

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China