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A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization

Junrong Zhang1, Huiming Tang1,2,3, Dwayne D Tannant4

  • 1Faculty of Engineering, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces a new machine learning model for accurate landslide displacement prediction. The novel approach combines Complete Ensemble Empirical Mode Decomposition (CEEMD) with optimized Support Vector Regression (SVR) for precise forecasting.

Keywords:
complete ensemble empirical mode decomposition (CEEMD)edit distance for real sequence (EDR)landslide displacement predictionmulti-swarm intelligence (MSI)support vector regression (SVR)

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Area of Science:

  • Geosciences
  • Computational Science
  • Engineering

Background:

  • Accurate landslide displacement prediction is critical for hazard mitigation.
  • Machine learning offers potential for improving forecast accuracy.

Purpose of the Study:

  • To propose a novel prediction model for enhanced landslide displacement accuracy.
  • To integrate advanced algorithms for superior predictive performance.

Main Methods:

  • Data preparation using Complete Ensemble Empirical Mode Decomposition (CEEMD) to separate trend and periodic displacements.
  • Feature extraction using CEEMD, t-test, and Edit Distance on Real Sequence (EDR).
  • Multi-Swarm Intelligence (MSI) optimization to tune Support Vector Regression (SVR) models for prediction.

Main Results:

  • The proposed model successfully predicted landslide displacements, matching observed data.
  • Demonstrated improvement in average relative error compared to existing methods.
  • Effective prediction of stepped displacement curves influenced by multiple factors.

Conclusions:

  • The novel MSI-optimized SVR model provides high-precision landslide displacement predictions.
  • The CEEMD-based decomposition and feature selection enhance predictive accuracy.
  • The model shows significant potential for real-world landslide monitoring and early warning systems.