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Updated: May 21, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Interpretable Machine Learning for Explaining and Predicting Collapse Hazards in the Changbai Mountain Region.

Xiangyang He1, Qiuling Lang1, Jiquan Zhang2

  • 1School of Jilin Emergency Management, Changchun Institute of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary

This study used machine learning to assess collapse hazards in Changbai Mountain, finding that an optimized Random Forest model best predicted risks. Distance from roads was identified as a critical factor for collapse management.

Keywords:
Changbai MountainSHAPcollapse hazard assessmentmachine learning

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Area of Science:

  • Geosciences
  • Environmental Science
  • Computer Science

Background:

  • Collapse hazards in the Changbai Mountain region result from complex interactions between geological, meteorological, and anthropogenic factors.
  • Effective hazard assessment requires advanced analytical techniques to handle intricate, nonlinear data structures.

Purpose of the Study:

  • To analyze collapse hazards by evaluating machine learning models for complex interactions.
  • To identify key risk factors and enhance the interpretability of hazard assessment models.

Main Methods:

  • Utilized a dataset of 651 collapse events to evaluate Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM).
  • Employed a variance inflation factor for optimizing collapse risk factor selection.
  • Integrated Shapley Additive Explanations (SHAP) with interpretable artificial intelligence to enhance model transparency.

Main Results:

  • The optimized Random Forest model demonstrated superior performance compared to SVM, XGBoost, and LightGBM.
  • Shapley Additive Explanations analysis identified 'distance from the road' as a significant factor influencing collapse hazard.
  • The study provides statistically validated performance metrics for the evaluated machine learning models.

Conclusions:

  • Machine learning, particularly the optimized Random Forest model, offers a robust approach for assessing complex collapse hazards.
  • Interpretable artificial intelligence methods like SHAP are crucial for understanding and managing collapse risks.
  • Findings highlight the importance of considering proximity to infrastructure in collapse management strategies.