Development of a Hybrid Method to Predict the Slope Surface Deformation Utilizing the Time Series and GA‐Elman Models

Abstract

A reliable prediction of the surface deformation of slopes is vital to better assess the fatalities and economic losses caused by landslides. Many prediction methods have been proposed to estimate the surface deformation of landslides with nonlinear characteristics. However, these methods have low accuracy and poor applicability. In this paper, a new hybrid method for surface deformation prediction was proposed, which was deduced from the Wavelet Analysis, Genetic Algorithm (GA), and Elman Algorithm. In this method, the slope surface deformation was decomposed into a trend component and a periodic component using the time series model, which were trained and predicted utilizing the GA-Elman model. The predicted slope surface deformation was the combination of the trend component and the periodic component. Then, the predicted results of slope surface deformation through GA-Elman were compared with the predicted results through Support Vector Machines (SVM), Extreme Learning Machine (ELM), Back Propagation (BP), and Genetic Algorithm-Back Propagation (GA-BP) models. The comparison was made with reference to the data retrieved from the on-site slopes and the laboratory tests. The results revealed that the proposed method highlighted reliability and could be used with higher accuracy to forecast the slope surface deformation in the process of landslides.

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

“…Among them, the selection of model parameters has a great influence on the decomposition accuracy. At present, the selection of machine learning model parameters is mainly iteratively optimized by particle swarm algorithm (PSO), genetic algorithm (GA) [26,27], whale optimization algorithm (WOA) [28] and so on. The sparrow search algorithm (SSA) has the advantages of fast convergence, high accuracy and robustness compared with other optimization algorithms [29].…”