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August 11, 2025Applied SciencesOpen Access

Comparative Performance of Machine Learning Models for Landslide Susceptibility Assessment: Impact of Sampling Strategies in Highway Buffer Zone

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Authors

ZTZ.M. TangSQShumao QiuHXHaoying Xia

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Overview

Comparative analysis shows GLHM sampling improves landslide model accuracy in a highway buffer zone, indicating better hazard mitigation strategies.

Key Points

  • GLHM sampling significantly improves AUROC and accuracy of machine learning models for landslide susceptibility assessment, enhancing model performance across various techniques.
  • The study achieved a maximum of 94.61% AUROC and 84.30% accuracy with XGBoost under GLHM sampling, showcasing its superior capability in landslide predictions.
  • Models were evaluated using ROC curves and 5-fold spatial cross-validation with substantial performance metrics, ensuring robust findings through rigorous testing methods.
  • Hazard-informed sampling strategies may enhance landslide susceptibility modeling, calling for further refinements in predictive assessments in vulnerable regions.

Cite This Study

Tang et al. (2025) studied this question.

synapsesocial.com/papers/68a360f20a429f7973329937https://doi.org/10.3390/app15158416
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