Singular spectrum analysis-based hybrid PSO-GSA-SVR model for predicting displacement of step-like landslides: a case of Jiuxianping landslide

As the displacement of step-like wading landslides is highly nonlinear and complex, it is difficult to develop a reasonable and accurate prediction model. Effective prediction of landslide displacement depends on the performance of the prediction model and the quality of monitoring data, which is greatly affected by sudden rainstorm and flood. To improve the prediction accuracy of the study model, Global Navigation Satellite System (GNSS) is used to monitor surface displacement. The GNSS-based displacement data are used to develop a hybrid model by combining Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Support Vector Regression (SVR). The displacement of Jiuxianping landslide, a typical wading rock landslide in Yunyang, China, has obvious step-like distribution characteristic. Firstly, the deformation characteristics and failure modes of Jiuxianping landslide are inductively analyzed. The step-like landslide displacement is decomposed into trend term and periodic term after reducing data noise by singular spectrum analysis (SSA). Then, a polynomial fitting model for the trend term prediction is developed, while multi-models are developed by PSO-SVR, GSA-SVR and PSO-GSA-SVR for predicting the periodic term. The three models were compared, and the sequence of removing the random term was evaluated again after it was reconstructed. Finally, the cumulative displacement was obtained by superimposing the trend displacement and the periodic displacement. Also, it was compared with the actual monitoring displacement. The results show that: (1) the step-like phenomenon of landslide displacement is mainly affected by rainfall and reservoir water level (RWL), and the displacement of the abrupt segment of the landslide exhibits an overall convex deformation; (2) SSA could effectively decompose the highly nonlinear step-like landslide displacement into trend term and periodic term; (3) the correlation coefficient of the hybrid-optimized PSO-GSA-SVR model for predicting the periodic displacement is more than 0.85, and the correlation coefficient of the overall displacement prediction model is 0.99. This work provides a better displacement prediction model for predicting a typical step-like wading rock landslide.