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Home
Research Domains
Engineering
Geomatic Engineering
Surveying (incl. Hydrographic Surveying)
Evaluation Of Geological Hazards Susceptibility Along A Key Railway Based On Machine Learning.

Home
Research Domains
Engineering
Geomatic Engineering
Surveying (incl. Hydrographic Surveying)
Evaluation Of Geological Hazards Susceptibility Along A Key Railway Based On Machine Learning.

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Evaluation of geological hazards susceptibility along a key railway based on machine learning.

Jiarong Liang^1,2, Wenwen Qi^3,4, Chong Xu^1,5

¹National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, 100085, China.

Scientific Reports

|November 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study mapped landslide susceptibility for the Hefei-Fuzhou High-Speed Railway using Random Forest (RF) modeling. High-risk zones correlate with steep slopes and heavy rainfall, guiding railway infrastructure safety.

Keywords:

Geological Hazards Hefei-Fuzhou High-Speed Railway Landslide Inventory Landslide Susceptibility

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Area of Science:

Geology
Geotechnical Engineering
Remote Sensing

Background:

Geological hazards like landslides threaten railway infrastructure.
High-speed railways are vital transportation links, especially in geologically complex regions.
Accurate landslide susceptibility mapping is crucial for infrastructure safety and disaster prevention.

Purpose of the Study:

To assess landslide susceptibility along a critical section of the Hefei-Fuzhou High-Speed Railway.
To develop and validate a predictive model for landslide occurrence.
To identify key factors contributing to landslide susceptibility in the region.

Main Methods:

Utilized a historical landslide inventory for model training.
Employed the Random Forest (RF) algorithm for susceptibility modeling.

Analyzed spatial correlation between high-risk zones and environmental factors like slope and precipitation.

Main Results:

The Random Forest model demonstrated excellent predictive performance.
A significant portion of the study area was identified in high and extremely high landslide susceptibility zones.
Steep slopes and high annual precipitation were found to be strongly correlated with high-risk areas.

Conclusions:

The developed landslide susceptibility map provides critical guidance for disaster prevention along the railway.
Slope was identified as the dominant influencing factor, with regional variations in other factors.
The study offers a valuable framework for infrastructure planning and geological risk management in similar developing regions.