A Hybrid Time Series Model for Predicting the Displacement of High Slope in the Loess Plateau Region

The physical and mechanical properties of the loess differ from other kinds of soil due to its collapsibility, which has resulted in the complex displacement development law of the loess slope. Therefore, the accurate estimation of the displacement of high slopes in a loess gully region is critical for the safety of people and in construction activities. In the present study, to improve the accuracy of traditional methods, the original cumulative displacement curve was decomposed into trend and fluctuation terms using Empirical Mode Decomposition (EMD) and Wavelet Decomposition (WD). Subsequently, the results were estimated using the Support Vector Machine (SVR) and Long Short-Term Memory Network (LSTM) optimized by Biogeography-based Optimization (BBO), respectively. To select the most appropriate model, SVR, LSTM, EMD-SVR-LSTM, EMD-BBO-SVR-LSTM, and WD-BBO-SVR-LSTM were employed to predict the deformation of a loess slope in the Loess Plateau of China. According to the results, the displacement increases rapidly at the starting stage, and then gradually stabilizes, which is the same as the trend in reality. On comparing the predicted results with field data, it was found that the models with decomposition algorithms achieved higher accuracy. Particularly, the determination coefficient of the EMD-BBO-SVR-LSTM model reaches 0.928, which has better algorithm stability and prediction accuracy than other models. In this study, the decomposition algorithm was applied to the loess slope displacement innovatively, and the appropriate machine learning algorithm adopted for the displacement components. The method improves the accuracy of prediction and provides a new idea for instability warning of loess excavation slopes. The research has implications for urban construction and sustainable development in loess mountainous areas.