Displacement prediction model of landslide based on ensemble empirical mode decomposition and support vector regression

Landslide displacement prediction is an important part of real-time monitoring and early warning of landslide disasters. A good landslide displacement prediction model is helpful to predict the occurrence of geological disasters. The deformation of the landslide is affected by a variety of external factors and presents the characteristics of randomness and nonlinearity. Among the existing landslide displacement prediction methods, machine learning methods have been widely used in landslide displacement prediction. Prediction of landslide displacement is the superposition of trend displacement and periodic displacement. In this study, the displacement extraction method of landslide trend term and period term based on integrated empirical mode decomposition (EEMD) and the support vector regression (SVR) model were used to predict the landslide displacement. The construction process and prediction performance of the model are introduced in detail, and the root mean square error (RMSE), average absolute error (MAE), average absolute percentage error (MAPE) and coefficient of determination (R2) are used as the predictive performance indicators of the evaluation model. The EEMD-SVR, SVR, and Elman models are used to predict the displacement of a landslide in the karst mountainous area of Guizhou province. The results showed that the RMSE, MAPE and R2 values of EEMD-SVR model were 0.648 mm, 0.518% and 0.996 8, respectively. This model can provide higher and more reliable landslide displacement prediction accuracy, and has certain reference value for the displacement prediction of similar landslide. © 2022 SinoMaps Press. All rights reserved.