Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping
Landslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study uti...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/16/5/657 |
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| author | Qi Zhang Zixin Ning Xiaohu Ding Junfeng Wu Zhao Wang Paraskevas Tsangaratos Ioanna Ilia Yukun Wang Wei Chen |
| author_facet | Qi Zhang Zixin Ning Xiaohu Ding Junfeng Wu Zhao Wang Paraskevas Tsangaratos Ioanna Ilia Yukun Wang Wei Chen |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | Landslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study utilized a hybrid ensemble-based methodology to improve prediction accuracy and effectively capture the complexity of landslide susceptibility patterns. This approach harnessed the power of ensemble models, employing a bagging algorithm with base learners, including the reduced error pruning decision tree (REPTree) and functional tree (FT) models. Ensemble models are particularly valuable because they combine the strengths of multiple models, enhancing the overall performance and robustness of the landslide susceptibility prediction. The study focused on Yanchuan County, situated within the hilly and gully region of China’s Loess Plateau, known for its susceptibility to landslides, using sixteen critical landslide conditioning factors, encompassing topographic, environmental, and geospatial variables, namely elevation, slope, aspect, proximity to rivers and roads, rainfall, the normalized difference vegetation index, soil composition, land use, and more. Model performances were evaluated and verified using a range of metrics, including receiver operating characteristic (ROC) curves, trade-off statistical metrics, and chi-square analysis. The results demonstrated the superiority of the integrated models, particularly the bagging FT (BFT) model, in accurately predicting landslide susceptibility, as evidenced by its high area under the curve area (AUC) value (0.895), compared to the other models. The model excelled in both positive predictive rate (0.847) and negative predictive rate (0.886), indicating its efficacy in identifying landslide and non-landslide areas and also in the F-score metric with a value of 0.869. The study contributes to the field of landslide risk assessment, offering a significant investigation tool for managing and mitigating landslide hazards in Yanchuan County and similar regions worldwide. |
| first_indexed | 2024-04-25T00:19:26Z |
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| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-25T00:19:26Z |
| publishDate | 2024-02-01 |
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| record_format | Article |
| series | Water |
| spelling | doaj.art-d65b1c9f35a64e08bff1f375fd30552e2024-03-12T16:57:35ZengMDPI AGWater2073-44412024-02-0116565710.3390/w16050657Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility MappingQi Zhang0Zixin Ning1Xiaohu Ding2Junfeng Wu3Zhao Wang4Paraskevas Tsangaratos5Ioanna Ilia6Yukun Wang7Wei Chen8College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaNo. 7 Oil Production Plant, Changqing Oilfield Company, PetroChina, Qingyang 745700, ChinaChangqing Oilfield Company, PetroChina, Xi’an 710021, ChinaNo. 7 Oil Production Plant, Changqing Oilfield Company, PetroChina, Qingyang 745700, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, GreeceShenmu Ningtiaota Coal Mining Co., Ltd., Shaanxi Coal and Chemical Industry Group Co., Ltd., Yulin 719300, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaLandslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study utilized a hybrid ensemble-based methodology to improve prediction accuracy and effectively capture the complexity of landslide susceptibility patterns. This approach harnessed the power of ensemble models, employing a bagging algorithm with base learners, including the reduced error pruning decision tree (REPTree) and functional tree (FT) models. Ensemble models are particularly valuable because they combine the strengths of multiple models, enhancing the overall performance and robustness of the landslide susceptibility prediction. The study focused on Yanchuan County, situated within the hilly and gully region of China’s Loess Plateau, known for its susceptibility to landslides, using sixteen critical landslide conditioning factors, encompassing topographic, environmental, and geospatial variables, namely elevation, slope, aspect, proximity to rivers and roads, rainfall, the normalized difference vegetation index, soil composition, land use, and more. Model performances were evaluated and verified using a range of metrics, including receiver operating characteristic (ROC) curves, trade-off statistical metrics, and chi-square analysis. The results demonstrated the superiority of the integrated models, particularly the bagging FT (BFT) model, in accurately predicting landslide susceptibility, as evidenced by its high area under the curve area (AUC) value (0.895), compared to the other models. The model excelled in both positive predictive rate (0.847) and negative predictive rate (0.886), indicating its efficacy in identifying landslide and non-landslide areas and also in the F-score metric with a value of 0.869. The study contributes to the field of landslide risk assessment, offering a significant investigation tool for managing and mitigating landslide hazards in Yanchuan County and similar regions worldwide.https://www.mdpi.com/2073-4441/16/5/657single-based and hybrid modelsbaggingreduced error pruning decision treefunction treeYanchuan County |
| spellingShingle | Qi Zhang Zixin Ning Xiaohu Ding Junfeng Wu Zhao Wang Paraskevas Tsangaratos Ioanna Ilia Yukun Wang Wei Chen Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping Water single-based and hybrid models bagging reduced error pruning decision tree function tree Yanchuan County |
| title | Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping |
| title_full | Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping |
| title_fullStr | Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping |
| title_full_unstemmed | Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping |
| title_short | Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping |
| title_sort | hybrid integration of bagging and decision tree algorithms for landslide susceptibility mapping |
| topic | single-based and hybrid models bagging reduced error pruning decision tree function tree Yanchuan County |
| url | https://www.mdpi.com/2073-4441/16/5/657 |
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