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July 15, 2025

Ensemble Machine Learning and GIS-Based Landslide Susceptibility Modeling: Insights from Fuyuan County

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Authors

XCXiaoyu CuiUniversity of Shanghai for Science and TechnologyWWWei WangNorth China University of Water Resources and Electric PowerLJLiu Jian-tingHenan University of Science and Technology

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Overview

Machine learning models improved landslide susceptibility mapping in Fuyuan County, suggesting stronger disaster risk management strategies.

Key Points

  • Ensemble machine learning models were evaluated for landslide susceptibility in Fuyuan County.
  • Random Forest achieved the highest predictive accuracy with a test AUC of 0.8420.
  • Feature importance analysis revealed lithology and mining density as key factors affecting landslide risk.
  • The findings offer a replicable framework for landslide early warning systems applicable in other regions.

Cite This Study

Cui et al. (2025) studied this question.

synapsesocial.com/papers/689a02b6e6551bb0af8cc49bhttps://doi.org/10.21203/rs.3.rs-6969350/v1
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  1. 1Landslide susceptibility modeling based on SHAP interpretability and ensemble learning: a case study in Fuyuan County, Southwest China2025 · 3 citations
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  5. 5GIS-based landslide susceptibility assessment using a random forest model: a case study of Ganzi and Aba, China2025