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September 5, 2025Frontiers in Earth ScienceOpen Access

Landslide susceptibility assessment of upper Yellow River using coupling statistical approaches, machine learning algorithms and SBAS-InSAR technique

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Authors

JZJin ZengQinghai UniversityWTWanbing TuoQinghai UniversityXWXinchao WangExtreme Networks (United States)

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Overview

Statistical methods and machine learning enhance landslide susceptibility models in the Yellow River, suggesting better disaster management strategies.

Key Points

  • The CF-XGBoost model significantly improves landslide susceptibility assessments in the Yellow River region.
  • ROC curve and Accuracy values indicate that integrated models outperform traditional methods in predicting landslide events.
  • Combining statistical approaches with machine learning algorithms effectively enhances predictive performance for landslide risks.
  • The study provides a reliable methodology for local disaster management authorities to formulate effective risk mitigation strategies.

Cite This Study

Zeng et al. (2025) studied this question.

synapsesocial.com/papers/68bb42212b87ece8dc958b69https://doi.org/10.3389/feart.2025.1652646
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Also Consider

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  1. 1Integration of SBAS-InSAR and RFE-RF-XGBoost for Landslide Vulnerability Assessment: A Case Study in Zhaotong City, Yongshan County2025
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  3. 3Ensemble Machine Learning and GIS-Based Landslide Susceptibility Modeling: Insights from Fuyuan County2025
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  5. 5Integration of information value with machine learning method for an enhanced predictive performance in landslide susceptibility mapping2026 · 2 citations