Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation

Abstract

Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.

Keywords

• slope deformation
• time-series data
• machine learning
• long-short-term memory (LSTM)
• transformer