Modelling and predicting landslide displacements and uncertainties by multiple machine-learning algorithms: application to Baishuihe landslide in Three Gorges Reservoir, China

Abstract

Time series landslide displacement is the most critical data set to understand landslide characteristics and infer its future development. To predict landslide displacements and their quantitative uncertainties, a mathematical description of the landslide evolution should be established. This paper proposes a novel hybrid machine-learning model to predict landslide displacements and quantify their uncertainties using prediction intervals (PIs). First, wavelet de-noising and Hodrick-Prescott (HP) filters are applied to decompose the original landslide displacement into periodic, trend, and noise components. Second, a module built on the framework of bootstrap and extreme learning machine (ELM) with a hybrid grey wolf optimizer (HGWO) is used to derive a formula for modelling the periodic component of the landslide motion. Another formula for predicting the trend component of the displacement is derived by double exponential smoothing (DES). Grey relational analysis and kernel principal component analysis (KPCA) are used to select the main factors controlling the landslide motions. Finally, the two constructed formulas are used to generate the predictions of landslide displacements and the PIs. Validation and comparison experiments have been carried out on the Baishuihe landslide in the Three Gorge Reservoir of China. Results demonstrate the proposed method can achieve better performance with higher-quality PIs than other state-of-the-art methods.

Keywords

• landslide displacement
• machine learning
• prediction intervals
• hybrid grey wolf optimizer
• extreme learning machine