Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide

• Published in: Journal of hydrology (Amsterdam) Vol. 628; p. 130588
• Main Authors: Song, Kanglei, Yang, Haiqing, Liang, Dan, Chen, Lichuan, Jaboyedoff, Michel
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2024
• Subjects: algorithms; climate change; deformation; Displacement prediction; Empirical Mode Decomposition; Failure mechanism; Geomorphic hazards; humans; hydrology; landforms; landslides; neural networks; prediction; rain; risk reduction; Slow-moving landslide; Step-like Displacement; Failure mechanism; Empirical Mode Decomposition; Slow-moving landslide; Geomorphic hazards; Displacement prediction; Step-like Displacement
• ISSN: 0022-1694
• DOI: 10.1016/j.jhydrol.2023.130588

[Display omitted] •A new method for slow displacement prediction of reservoir landslide is proposed.•The deformation mechanism of different regions of landslide is different.•The Jiuxianping landslide is affected by the combination of rainfall and reservoir.•The mechanism of step-like slow displacement of reservoir landslide is discussed.•Empirical mode decomposition can effectively improve the prediction model accuracy. Landslides triggered by extreme rainfall due to global climate change are becoming more frequent. The Earth surface processes activity and landform evolution caused by landslide movement seriously affect human survival and the environment. In recent years, the reservoir slope located in the Three Gorges reservoir (TGR) area has been subject to the combined effects of extreme rainfall and reservoir regulation, which have been highly susceptible to inducing landslides. Reservoir landslides usually show step-like slow movement characteristics. The causes of slow-moving landslides are complex, the mechanisms behind them are difficult to grasp, and it may evolve into violent landslide disasters. Therefore, accurately predicting step-like displacement in slow-moving landslides is an effective solution for risk reduction. To achieve the accurate prediction of the step-like displacement of slow-moving landslides and the analysis of the failure mechanism, this study establishes a prediction model applicable to step-like displacement prediction of slow-moving reservoir landslides is developed based on 16 years of continuously monitored cumulative displacement of the Jiuxianping landslide, combined with the Empirical Mode Decomposition (EMD) method and the Improved Particle Swarm Optimization (IPSO) optimized the Long short-term memory (LSTM) neural network. This approach can effectively improve step-like displacement prediction accuracy. In particular, this approach can better represent the association between the step displacement of slow-moving landslides with environmental characteristics (e.g., rainfall, reservoir level). The study reveals that there are differences in the mechanisms and influencing conditions that cause the deformation of different areas of the landslide. Additionally, the reservoir water level fluctuation and seasonal rainfall are identified as the primary reasons contributing to the stepped displacement of the Jiuxianping landslide. The method provides a basis for the step-like displacement prediction, instability mechanism and landform evolution study of slow-moving reservoir landslides and provides valuable guidance for the prevention and control of similar reservoir geomorphic hazards worldwide.