Research on Slope Early Warning and Displacement Prediction Based on Multifractal Characterization

• Published in: Fractal and fractional 2024-09, Vol.8 (9), p.522
• Main Authors: Sun, Xiaofei, Su, Ying, Yang, Chengtao, Tan, Junzhe, Liu, Dunwen
• Format: Article
• Language: English
• Subjects: Algorithms; China; Data analysis; Deformation; Deformation analysis; deformation warning; Disasters; Emergency communications systems; Engineering; Error analysis; Geology; India; Landslides; Landslides & mudslides; Machine learning; Monitoring; multiple fractal theory; Particle swarm optimization; PSO-LSTM; Roads & highways; Root-mean-square errors; Slope stability; Stability criteria; Surface layers; Test methods; Time series; Transportation authorities; tunnel deformation
• ISSN: 2504-3110; 2504-3110
• DOI: 10.3390/fractalfract8090522

The occurrence of landslide hazards significantly induces changes in slope surface displacement. This study conducts an in-depth analysis of the multifractal characteristics and displacement prediction of highway slope surface displacement sequences. Utilizing automated monitoring devices, data are collected to analyze the deformation patterns of the slope surface layer. Specifically, the multifractal detrended fluctuation analysis (MF-DFA) method is employed to examine the multifractal features of the monitoring data for slope surface displacement. Additionally, the Mann–Kendall (M-K) method is combined to construct the α indicator and f(α) indicator criteria, which provide early warnings for slope stability. Furthermore, the long short-term memory (LSTM) model is optimized using the particle swarm optimization (PSO) algorithm to enhance the prediction of slope surface displacement. The results indicate that the slope displacement monitoring data exhibit a distinct fractal sequence characterized by h(q), with values decreasing as the fluctuation function q decreases. Through this study, the slope landslide warning classification has been determined to be Level III. Moreover, the PSO-LSTM model demonstrates superior prediction accuracy and stability in slope displacement forecasting, achieving a root mean square error (RMSE) of 0.72 and a coefficient of determination (R2) of 91%. Finally, a joint response synthesis of the slope landslide warning levels and slope displacement predictions resulted in conclusions. Subsequent surface displacements of the slope are likely to stabilize, indicating the need for routine monitoring and inspection of the site.