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Deep learning can predict global earthquake-triggered landslides.

Xuanmei Fan¹, Xin Wang¹, Chengyong Fang¹

¹State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China.

National Science Review

|June 16, 2025

Summary

This summary is machine-generated.

Scientists created a global landslide database and used deep learning to predict earthquake-triggered landslides worldwide. This tool offers rapid, accurate hazard assessments, improving disaster response and planning.

Keywords:

deep learning earthquake-triggered landslide global database landslide prediction model

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Area of Science:

Geosciences
Geohazards
Computational Seismology

Background:

Earthquake-triggered landslides are a major lethal hazard, necessitating rapid response to prevent cascading disasters.
Current prediction methods are hampered by simplified models, limited regional data, and retrospective analyses, hindering timely hazard assessment.
Effective geohazard prediction requires comprehensive global data and advanced analytical techniques.

Purpose of the Study:

To develop a comprehensive global database of earthquake-triggered landslides.
To create advanced deep-learning models for rapid, accurate landslide probability prediction following earthquakes.
To provide a scalable tool for immediate disaster evaluation and pre-event hazard planning.

Main Methods:

Compiled a global database of approximately 400,000 landslides from 38 major earthquakes over the past 50 years.
Developed and applied advanced deep-learning models to predict landslide probability.
Validated model performance for rapid, worldwide hazard assessment.

Main Results:

Achieved an average spatial accuracy of approximately 82% in landslide prediction.
Demonstrated the ability to predict landslide probability in under a minute for any global earthquake.
Successfully bypassed the need for prior local knowledge in hazard assessments.

Conclusions:

The developed framework offers a transformative advance in global geohazard prediction.
Enables swift disaster evaluation and enhances pre-event hazard planning for earthquake-triggered landslides.
Provides a scalable and efficient tool to mitigate the catastrophic impacts of seismic landslides.