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Machine learning-based analyzing earthquake-induced slope displacement

Author

Listed:
  • Jiyu Wang
  • Niaz Muhammad Shahani
  • Xigui Zheng
  • Jiang Hongwei
  • Xin Wei

Abstract

Accurately evaluating earthquake-induced slope displacement is a key factor for designing slopes that can effectively respond to seismic activity. This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. A dataset of 45 samples was used, with 70% allocated for training and 30% for testing. To improve model robustness, repeated 5-fold cross-validation was applied. Among the models, XGBoost demonstrated superior predictive accuracy, with an R2 value of 0.99 on both the train and test data, outperforming ANN, SVM, and RF, which had R2 values of 0.63 and 0.80, 0.87 and 0.86, 0.94 and 0.87 on the train and test data, respectively. Sensitivity analysis identified maximum horizontal acceleration (kmax = 0.714) as the most influential factor in slope displacement. The findings suggest that the XGBoost model developed in this study is highly effective in predicting earthquake-induced slope displacement, offering valuable insights for early warning systems and slope stability management.

Suggested Citation

  • Jiyu Wang & Niaz Muhammad Shahani & Xigui Zheng & Jiang Hongwei & Xin Wei, 2025. "Machine learning-based analyzing earthquake-induced slope displacement," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-14, February.
    • Handle: RePEc:plo:pone00:0314977
    DOI: 10.1371/journal.pone.0314977
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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