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EoML-SlideNet: A Lightweight Framework for Landslide Displacement Forecasting with Multi-Source Monitoring Data.

Fan Zhang1,2, Yuanfa Ji1, Xiaoming Liu3

  • 1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.

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|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces EoML-SlideNet, a lightweight framework for accurate landslide displacement forecasting on edge devices. It offers faster, more responsive early warning systems for vulnerable karst terrains.

Keywords:
DBLE-LVEoML-SlideNetFLOPsinference timelandslide displacement forecastinglightweight edge-level modeling

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Area of Science:

  • Geosciences
  • Earthquake Engineering
  • Artificial Intelligence

Background:

  • Karst terrains in Guangxi, China, face high landslide risk due to rainfall.
  • Existing landslide forecasting systems suffer from latency and computational demands on edge devices.

Purpose of the Study:

  • To develop a lightweight forecasting framework (EoML-SlideNet) for resource-limited edge hardware.
  • To improve the timeliness and accuracy of landslide displacement prediction for early warning systems.

Main Methods:

  • EoML-SlideNet decomposes displacement into trend and periodic components.
  • It utilizes a Dual-Band Lasso-Enhanced Latent Variable (DBLE-LV) module for feature selection.
  • Lightweight autoregressive and neural network models predict trend and periodic components, respectively.

Main Results:

  • EoML-SlideNet achieved 2-4 times lower MAE/RMSE compared to deep learning and baseline models.
  • Inference speed was 3-30 times faster, with significantly lower floating-point operations (FLOPs).
  • The framework demonstrated suitability for edge deployment without remote server dependency.

Conclusions:

  • Low-complexity models can match or exceed deep network accuracy for landslide prediction.
  • EoML-SlideNet provides a practical solution for real-time, edge-based landslide early warning systems.
  • The study highlights the importance of FLOPs and runtime as practical evaluation metrics.