Machine learning-based analyzing earthquake-induced slope displacement.
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314977 |
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| _version_ | 1823856862877974528 |
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| author | Jiyu Wang Niaz Muhammad Shahani Xigui Zheng Jiang Hongwei Xin Wei |
| author_facet | Jiyu Wang Niaz Muhammad Shahani Xigui Zheng Jiang Hongwei Xin Wei |
| author_sort | Jiyu Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-daffcbbd64764f5c8c4be6a4159bc575 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-daffcbbd64764f5c8c4be6a4159bc5752025-02-12T05:30:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031497710.1371/journal.pone.0314977Machine learning-based analyzing earthquake-induced slope displacement.Jiyu WangNiaz Muhammad ShahaniXigui ZhengJiang HongweiXin WeiAccurately 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.https://doi.org/10.1371/journal.pone.0314977 |
| spellingShingle | Jiyu Wang Niaz Muhammad Shahani Xigui Zheng Jiang Hongwei Xin Wei Machine learning-based analyzing earthquake-induced slope displacement. PLoS ONE |
| title | Machine learning-based analyzing earthquake-induced slope displacement. |
| title_full | Machine learning-based analyzing earthquake-induced slope displacement. |
| title_fullStr | Machine learning-based analyzing earthquake-induced slope displacement. |
| title_full_unstemmed | Machine learning-based analyzing earthquake-induced slope displacement. |
| title_short | Machine learning-based analyzing earthquake-induced slope displacement. |
| title_sort | machine learning based analyzing earthquake induced slope displacement |
| url | https://doi.org/10.1371/journal.pone.0314977 |
| work_keys_str_mv | AT jiyuwang machinelearningbasedanalyzingearthquakeinducedslopedisplacement AT niazmuhammadshahani machinelearningbasedanalyzingearthquakeinducedslopedisplacement AT xiguizheng machinelearningbasedanalyzingearthquakeinducedslopedisplacement AT jianghongwei machinelearningbasedanalyzingearthquakeinducedslopedisplacement AT xinwei machinelearningbasedanalyzingearthquakeinducedslopedisplacement |