Journal Article

Machine learning-based analyzing earthquake-induced slope displacement

Wang, Jiyu, Shahani, Niaz Muhammad, Wei, Xin, Zheng, Xigui, Hongwei, Jiang

2025

Overview

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 R 2 value of 0.99 on both the train and test data, outperforming ANN, SVM, and RF, which had R 2 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Algorithms / Analysis / Artificial neural networks / Biology and Life Sciences / China / Computer and Information Sciences / Datasets / Deformation / Displaced persons / Early warning systems / Earthquake prediction / Earthquakes / Engineering and Technology / Environment / Environmental aspects / Failure / Forecasts and trends / Humans / Learning algorithms / Machine Learning / Monte Carlo simulation / Neural networks / Neural Networks, Computer / Oil recovery / Performance evaluation / Physical Sciences / Research and Analysis Methods / Seismic activity / Seismic response / Seismic stability / Sensitivity analysis / Slope stability / Slopes (Physical geography) / Support Vector Machine / Support vector machines / Testing / Variables / Warning systems