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Landslide Hazard Identification and Prediction in Complex Mountainous Areas Using Ascending and Descending Orbits

Wenmiao Zhao1,2, Pengfei Cong1, Xu Ma3

  • 1Langfang Integrated Natural Resources Survey Center, China Geological Survey, Langfang 065000, China.

Sensors (Basel, Switzerland)
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PubMed
Summary

This study introduces a new framework using satellite data and deep learning to identify and predict landslides in complex mountains. The advanced model significantly improves prediction accuracy, aiding early warning systems.

Keywords:
LSTMSBAS-InSARdeformation predictionlandslide identification

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Area of Science:

  • Geosciences
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Time-series Interferometric Synthetic Aperture Radar (InSAR) is crucial for landslide monitoring.
  • Complex mountainous terrain presents challenges like geometric distortions and intricate deformation mechanisms for InSAR analysis.

Purpose of the Study:

  • To develop an integrated landslide identification and prediction framework for complex mountainous regions.
  • To enhance the accuracy of landslide monitoring and early warning systems.

Main Methods:

  • Integration of ascending and descending Sentinel-1A InSAR data using the Small Baseline Subset InSAR (SBAS-InSAR) method.
  • Application of wavelet decomposition and Gray relational analysis for time-series analysis and factor selection.
  • Development of a physics-guided deep learning model (WT-LSTM) for landslide prediction.

Main Results:

  • Identified 41 hazardous landslide sites in Yangbi County, Yunnan Province.
  • The proposed WT-LSTM model demonstrated superior prediction accuracy (RMSE = 1.16-2.19 mm) compared to standalone LSTM and Support Vector Regression (SVR) models.
  • Successfully extracted and analyzed landslide displacement characteristics along slope aspects.

Conclusions:

  • The integrated framework effectively addresses challenges in landslide monitoring in complex mountainous areas.
  • The WT-LSTM model shows significant potential for improving landslide deformation prediction and early warning.
  • Findings provide a valuable reference for geological hazard risk assessment and management.