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Updated: May 29, 2025

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Machine learning-based analyzing earthquake-induced slope displacement.

Jiyu Wang1, Niaz Muhammad Shahani1, Xigui Zheng1

  • 1School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu Province, China.

Plos One
|February 6, 2025
PubMed
Summary
This summary is machine-generated.

The XGBoost machine learning model accurately predicts earthquake-induced slope displacement, outperforming other models. Maximum horizontal acceleration is the key factor influencing displacement, aiding slope stability management.

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Area of Science:

  • Geotechnical Engineering
  • Seismology
  • Machine Learning

Background:

  • Accurate evaluation of earthquake-induced slope displacement is crucial for seismic slope design.
  • Various machine learning models are explored for their efficacy in this domain.

Purpose of the Study:

  • To evaluate and compare the performance of Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models.
  • To identify the most effective machine learning model for predicting earthquake-induced slope displacement.

Main Methods:

  • A dataset of 45 samples was utilized, with 70% for training and 30% for testing.
  • Repeated 5-fold cross-validation was employed to enhance model robustness.
  • Model performance was assessed using R2 values and sensitivity analysis.

Main Results:

  • XGBoost achieved a superior R2 value of 0.99 on both training and testing data.
  • ANN, SVM, and RF models showed lower R2 values (0.63-0.94 for training, 0.80-0.87 for testing).
  • Sensitivity analysis revealed maximum horizontal acceleration (kmax) as the most significant factor influencing slope displacement.

Conclusions:

  • The XGBoost model demonstrates high predictive accuracy for earthquake-induced slope displacement.
  • The developed XGBoost model offers valuable insights for seismic early warning systems and slope stability management.
  • Maximum horizontal acceleration is a critical parameter for predicting slope displacement during seismic events.