Hybrid Landslide Displacement Prediction via Improved Optimization

This study proposes a hybrid landslide displacement prediction model based on multi-strategy integrated optimization to address high nonlinearity and limited accuracy. An improved SFOA with Lévy flight, dynamic exploration adjustment, and stagnation detection enhances global search and convergence. The optimized SFOA (OSFOA) is employed to optimize CEEMDAN using minimum envelope entropy, reducing hyperparameter subjectivity and decomposing cumulative displacement into multi-scale components. The trend term is predicted by a Bayesian-optimized ARIMA, while periodic and stochastic terms are further decomposed by VMD and predicted using Bayesian-optimized SVR. GRA-MIC is applied to select key influencing factors and optimize model inputs. Results show that the proposed method improves accuracy and stability, reducing RMSE by about 82% and 52% compared with SSA-SVR and the baseline single decomposition model, respectively. The study further identifies monthly rainfall change and two-month reservoir level variation as the dominant driving factors for the displacement evolution, providing an effective and interpretable approach for complex landslide early warning.