Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
Current research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. In the Three Gorges Reservoir area of China, a region prone to frequent landslides that endanger lives and infrastructure, this study advances lands...
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MDPI AG
2025-05-01
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| author | Lizhou Zhang Siqiao Ye Deping He Linfeng Wang Weiping Li Bijing Jin Taorui Zeng |
| author_facet | Lizhou Zhang Siqiao Ye Deping He Linfeng Wang Weiping Li Bijing Jin Taorui Zeng |
| author_sort | Lizhou Zhang |
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| description | Current research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. In the Three Gorges Reservoir area of China, a region prone to frequent landslides that endanger lives and infrastructure, this study advances landslide susceptibility prediction by integrating ensemble learning with systematic factor engineering. Four homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and two heterogeneous ensembles (bagging and stacking) were implemented to evaluate 14 influencing factors. The key results demonstrate the Digital Elevation Model (DEM) as the dominant factor, while the stacking ensemble achieved superior performance (AUC = 0.876), outperforming single models by 4.4%. Iterative factor elimination and hyperparameter tuning increased the high-susceptibility zones in the stacking predictions to 42.54% and enhanced XGBoost’s low-susceptibility classification accuracy from 12.96% to 13.57%. The optimized models were used to generate a high-resolution landslide susceptibility map, identifying 23.8% of the northern and central regions as high-susceptibility areas, compared to only 9.3% as eastern and southern low-susceptibility zones. This methodology improved the prediction accuracy by 12–18% in comparison to a single model, providing actionable insights for landslide risk mitigation. |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-cdd92bf34323465a9edbff02522ee2c42025-08-20T02:32:54ZengMDPI AGApplied Sciences2076-34172025-05-011511619210.3390/app15116192Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor EngineeringLizhou Zhang0Siqiao Ye1Deping He2Linfeng Wang3Weiping Li4Bijing Jin5Taorui Zeng6College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400047, ChinaCollege of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400047, ChinaChongqing Institute of Surveying and Mapping Science and Technology, Chongqing 401121, ChinaCollege of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400047, ChinaChongqing Institute of Surveying and Mapping Science and Technology, Chongqing 401121, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaInstitute of Future Civil Engineering Science and Technology, Chongqing Jiaotong University, Chongqing 400047, ChinaCurrent research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. In the Three Gorges Reservoir area of China, a region prone to frequent landslides that endanger lives and infrastructure, this study advances landslide susceptibility prediction by integrating ensemble learning with systematic factor engineering. Four homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and two heterogeneous ensembles (bagging and stacking) were implemented to evaluate 14 influencing factors. The key results demonstrate the Digital Elevation Model (DEM) as the dominant factor, while the stacking ensemble achieved superior performance (AUC = 0.876), outperforming single models by 4.4%. Iterative factor elimination and hyperparameter tuning increased the high-susceptibility zones in the stacking predictions to 42.54% and enhanced XGBoost’s low-susceptibility classification accuracy from 12.96% to 13.57%. The optimized models were used to generate a high-resolution landslide susceptibility map, identifying 23.8% of the northern and central regions as high-susceptibility areas, compared to only 9.3% as eastern and southern low-susceptibility zones. This methodology improved the prediction accuracy by 12–18% in comparison to a single model, providing actionable insights for landslide risk mitigation.https://www.mdpi.com/2076-3417/15/11/6192landslide susceptibilityensemble learningfactor engineeringrandom forestXGBoostLightGBM |
| spellingShingle | Lizhou Zhang Siqiao Ye Deping He Linfeng Wang Weiping Li Bijing Jin Taorui Zeng Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering Applied Sciences landslide susceptibility ensemble learning factor engineering random forest XGBoost LightGBM |
| title | Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering |
| title_full | Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering |
| title_fullStr | Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering |
| title_full_unstemmed | Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering |
| title_short | Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering |
| title_sort | examination of landslide susceptibility modeling using ensemble learning and factor engineering |
| topic | landslide susceptibility ensemble learning factor engineering random forest XGBoost LightGBM |
| url | https://www.mdpi.com/2076-3417/15/11/6192 |
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