Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip
<p>Accurate prediction of water inrush volumes is essential for safeguarding tunnel construction operations. This study proposes a method for predicting tunnel water inrush volumes, leveraging the eXtreme Gradient Boosting (XGBoost) model optimized with Bayesian techniques. To maximize the uti...
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2025
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| _version_ | 1852021045812264960 |
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| author | Shengdong Ju (21178127) |
| author2 | Guangzhao Ou (21178130) Tao Peng (81319) Yanning Wang (5143841) Quanlin Song (21178133) Peng Guan (102746) |
| author2_role | author author author author author |
| author_facet | Shengdong Ju (21178127) Guangzhao Ou (21178130) Tao Peng (81319) Yanning Wang (5143841) Quanlin Song (21178133) Peng Guan (102746) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shengdong Ju (21178127) Guangzhao Ou (21178130) Tao Peng (81319) Yanning Wang (5143841) Quanlin Song (21178133) Peng Guan (102746) |
| dc.date.none.fl_str_mv | 2025-04-25T05:21:38Z |
| dc.identifier.none.fl_str_mv | 10.3389/feart.2025.1590203.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_1_Tunnel_water_inflow_prediction_using_explainable_machine_learning_and_augmented_partially_missing_dataset_zip/28863425 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Solid Earth Sciences tunnel water inflow XGBoost bayesian optimization data augmentation model interpretation |
| dc.title.none.fl_str_mv | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Accurate prediction of water inrush volumes is essential for safeguarding tunnel construction operations. This study proposes a method for predicting tunnel water inrush volumes, leveraging the eXtreme Gradient Boosting (XGBoost) model optimized with Bayesian techniques. To maximize the utility of available data, 654 datasets with missing values were imputed and augmented, forming a robust dataset for the training and validation of the Bayesian optimized XGBoost (BO-XGBoost) model. Furthermore, the SHapley Additive explanations (SHAP) method was employed to elucidate the contribution of each input feature to the predictive outcomes. The results indicate that: (1) The constructed BO-XGBoost model exhibited exceptionally high predictive accuracy on the test set, with a root mean square error (RMSE) of 7.5603, mean absolute error (MAE) of 3.2940, mean absolute percentage error (MAPE) of 4.51%, and coefficient of determination (R<sup>2</sup>) of 0.9755; (2) Compared to the predictive performance of support vector mechine (SVR), decision tree (DT), and random forest (RF) models, the BO-XGBoost model demonstrates the highest R<sup>2</sup> values and the smallest prediction error; (3) The input feature importance yielded by SHAP is groundwater level (h) > water-producing characteristics (W) > tunnel burial depth (H) > rock mass quality index (RQD). The proposed BO-XGBoost model exhibited exceptionally high predictive accuracy on the tunnel water inrush volume prediction dataset, thereby aiding managers in making informed decisions to mitigate water inrush risks and ensuring the safe and efficient advancement of tunnel projects.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a70dbd2b7c19428058505c6e6e366266 |
| identifier_str_mv | 10.3389/feart.2025.1590203.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28863425 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zipShengdong Ju (21178127)Guangzhao Ou (21178130)Tao Peng (81319)Yanning Wang (5143841)Quanlin Song (21178133)Peng Guan (102746)Solid Earth Sciencestunnel water inflowXGBoostbayesian optimizationdata augmentationmodel interpretation<p>Accurate prediction of water inrush volumes is essential for safeguarding tunnel construction operations. This study proposes a method for predicting tunnel water inrush volumes, leveraging the eXtreme Gradient Boosting (XGBoost) model optimized with Bayesian techniques. To maximize the utility of available data, 654 datasets with missing values were imputed and augmented, forming a robust dataset for the training and validation of the Bayesian optimized XGBoost (BO-XGBoost) model. Furthermore, the SHapley Additive explanations (SHAP) method was employed to elucidate the contribution of each input feature to the predictive outcomes. The results indicate that: (1) The constructed BO-XGBoost model exhibited exceptionally high predictive accuracy on the test set, with a root mean square error (RMSE) of 7.5603, mean absolute error (MAE) of 3.2940, mean absolute percentage error (MAPE) of 4.51%, and coefficient of determination (R<sup>2</sup>) of 0.9755; (2) Compared to the predictive performance of support vector mechine (SVR), decision tree (DT), and random forest (RF) models, the BO-XGBoost model demonstrates the highest R<sup>2</sup> values and the smallest prediction error; (3) The input feature importance yielded by SHAP is groundwater level (h) > water-producing characteristics (W) > tunnel burial depth (H) > rock mass quality index (RQD). The proposed BO-XGBoost model exhibited exceptionally high predictive accuracy on the tunnel water inrush volume prediction dataset, thereby aiding managers in making informed decisions to mitigate water inrush risks and ensuring the safe and efficient advancement of tunnel projects.</p>2025-04-25T05:21:38ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/feart.2025.1590203.s001https://figshare.com/articles/dataset/Data_Sheet_1_Tunnel_water_inflow_prediction_using_explainable_machine_learning_and_augmented_partially_missing_dataset_zip/28863425CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288634252025-04-25T05:21:38Z |
| spellingShingle | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip Shengdong Ju (21178127) Solid Earth Sciences tunnel water inflow XGBoost bayesian optimization data augmentation model interpretation |
| status_str | publishedVersion |
| title | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| title_full | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| title_fullStr | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| title_full_unstemmed | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| title_short | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| title_sort | Data Sheet 1_Tunnel water inflow prediction using explainable machine learning and augmented partially missing dataset.zip |
| topic | Solid Earth Sciences tunnel water inflow XGBoost bayesian optimization data augmentation model interpretation |