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November 20, 2025Geomatics Natural Hazards and RiskOpen Access

GIS-based landslide susceptibility assessment using a random forest model: a case study of Ganzi and Aba, China

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Authors

YZYing ZhouLZLu ZhangYFYuan Feng

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Overview

GIS-based assessment shows enhanced landslide hazard identification in Ganzi and Aba using random forest and logistic regression.

Key Points

  • The aim is to assess landslide susceptibility and enhance hazard prevention in Ganzi and Aba, China.
  • Conducted GIS-based landslide susceptibility mapping using random forest and logistic regression models.
  • Evaluated 13 conditioning factors influencing landslide occurrences.
  • Used geological data and landslide information for model input.
  • Validated model performance with receiver operating characteristic (ROC) curve analysis.
  • Random forest model achieved superior predictive accuracy with an AUC of 0.972.
  • Highest susceptibility areas identified in Wenchuan, Mao, Jiuzhaigou, and Luding Counties.
  • Elevation and distance to roads significantly influence landslide risk.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/6924f07ac0ce034ddc34ff27https://doi.org/10.1080/19475705.2025.2583459
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