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A semantic information-driven stepwise landslide displacement prediction model.

Feifei Tang1, Tianjun Tang2, Hongzhou Zhu3

  • 1School of Smart City, Chongqing Jiaotong University, Chongqing, 40074, China. tangfeifei@cqjtu.edu.cn.

Environmental Monitoring and Assessment
|September 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semantic information-driven model for predicting landslide displacement. It accurately identifies and quantifies landslide acceleration phases, improving early warning systems.

Keywords:
Acceleration phaseDisplacement phase identificationDisplacement predictionSemantic informationStepwise landslide

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Area of Science:

  • Geotechnical Engineering
  • Geohazards
  • Predictive Modeling

Background:

  • Landslide prediction is crucial for early warning systems.
  • Existing data-driven models struggle with overfitting and accurately predicting rapid displacement increases during acceleration phases.

Purpose of the Study:

  • To develop a semantic information-driven model for stepwise landslide displacement prediction.
  • To improve the identification and quantitative prediction of landslide acceleration phases.

Main Methods:

  • Converting raw landslide monitoring data into text-based semantic information.
  • Fusing semantic features and applying a sliding window algorithm to divide landslide phases (stationary and acceleration).
  • Identifying the landslide phase and predicting displacement during the acceleration phase.

Main Results:

  • The model successfully identifies the landslide acceleration phase qualitatively using semantic information.
  • The model quantitatively predicts daily displacement during the acceleration phase.
  • Experimental results on Chinese landslides validate the model's effectiveness.

Conclusions:

  • The proposed semantic information-driven model offers a valuable approach for early warning of stepwise landslides.
  • This method enhances the accuracy of predicting rapid displacement changes, addressing limitations of traditional data-driven models.