ArticleScientific reports2024
A dynamic prediction model of landslide displacement based on VMD-SSO-LSTM approach.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A time series based dynamic prediction model for loess mudstone landslides.Scientific reports · 2026Article
- Bayesian-Inspired Dynamic-Lag Causal Graphs and Role-Aware Transformers for Landslide Displacement Forecasting.Entropy (Basel, Switzerland) · 2025Article
- A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction.Sensors (Basel, Switzerland) · 2025Article
- EoML-SlideNet: A Lightweight Framework for Landslide Displacement Forecasting with Multi-Source Monitoring Data.Sensors (Basel, Switzerland) · 2025Article
- High-precision monitoring and prediction of mining area surface subsidence using SBAS-InSAR and CNN-BiGRU-attention model.Scientific reports · 2024Article
- A two-stage subsynchronous oscillation assessment method for DFIG-based wind farm grid-connected system.Scientific reports · 2024Article
- The forecasting of surface displacement for tunnel slopes utilizing the WD-IPSO-GRU model.Scientific reports · 2024Article
- Short-term natural gas load forecasting based on EL-VMD-Transformer-ResLSTM.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD-SSO-LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO-LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD-SSO-LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R
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Registered trials
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