Prediction of landslide step-like displacement using factor preprocessing-based hybrid optimized SVR model in the Three Gorges Reservoir, China

Abstract

Artificial intelligence (AI) is gaining popularity in landslide displacement modeling due to its strong generalization capacity and ability to accurately represent complex, nonlinear laws. However, few studies have examined the validity of factor selection and the impact of data noise on the model. To address these issues, we employ various analyses, including Pearson correlation coefficient and grey correlation degree analyses to diagnose redundant and highly correlated factors, respectively. Singular spectrum analysis (SSA) is employed for denoising the data, leveraging the benefits of the hankel matrix and minimal parameters. This technique facilitates the decomposition of displacement into trend and periodic terms. The trend term displacement is fitted with a polynomial, and the periodic term displacement is predicted using a support vector regression (SVR) model. The hybrid-optimized SVR by particle swarm optimization-gravitational search algorithm (PSO-GSA) is used to improve the model's performance and prediction ability. We apply the proposed method to the Bazimen and Baishuihe landslides in Zigui County for model validation. The results show that denoising the input factor data reduces the root mean square error (RMSE) and mean absolute error (MAE) and increases the correlation coefficient (R). Furthermore, the PSO-GSA algorithm exhibits excellent generalization ability and fast optimization speed in optimizing parameters. The correlation coefficient increase to 0.99 when using the hybrid-optimized SVR model for displacement prediction.