Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China
Traditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the stren...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2326005 |
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| author | Lei-Lei Liu Aasim Danish Xiao-Mi Wang Wen-Qing Zhu |
| author_facet | Lei-Lei Liu Aasim Danish Xiao-Mi Wang Wen-Qing Zhu |
| author_sort | Lei-Lei Liu |
| collection | DOAJ |
| description | Traditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. Subsequently, we integrate the extreme gradient boosting (XGB), random forest (RF) and gradient boosting decision tree (GBDT) models with SVC to create hybrid approaches. In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. The results demonstrated that the stacking ensemble model outperforms traditional models. The XBG+SVC model achieves the highest AUC value (0.9468), which is followed by the GBDT+SVC (0.9316), RF+SVC (0.9162), XGB (0.9393), GBDT (0.9009), and RF (0.8693). These findings indicate that stacking machine learning approaches hold promise for landslide susceptibility mapping. |
| format | Article |
| id | doaj-art-bce6e3ee2db843e89d898e97c9cb7d98 |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-bce6e3ee2db843e89d898e97c9cb7d982024-12-10T08:23:08ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2326005Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, ChinaLei-Lei Liu0Aasim Danish1Xiao-Mi Wang2Wen-Qing Zhu3Ministry of Education, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Changsha, PR ChinaMinistry of Education, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Changsha, PR ChinaSchool of Geographic Science, Hunan Normal University, Changsha, PR ChinaMinistry of Education, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Changsha, PR ChinaTraditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. Subsequently, we integrate the extreme gradient boosting (XGB), random forest (RF) and gradient boosting decision tree (GBDT) models with SVC to create hybrid approaches. In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. The results demonstrated that the stacking ensemble model outperforms traditional models. The XBG+SVC model achieves the highest AUC value (0.9468), which is followed by the GBDT+SVC (0.9316), RF+SVC (0.9162), XGB (0.9393), GBDT (0.9009), and RF (0.8693). These findings indicate that stacking machine learning approaches hold promise for landslide susceptibility mapping.https://www.tandfonline.com/doi/10.1080/10106049.2024.2326005Landslide susceptibility assessmentstacking ensemble machine learningextreme gradient boostingsupport vector classifiermachine learning |
| spellingShingle | Lei-Lei Liu Aasim Danish Xiao-Mi Wang Wen-Qing Zhu Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China Geocarto International Landslide susceptibility assessment stacking ensemble machine learning extreme gradient boosting support vector classifier machine learning |
| title | Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China |
| title_full | Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China |
| title_fullStr | Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China |
| title_full_unstemmed | Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China |
| title_short | Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China |
| title_sort | ensemble stacking a powerful tool for landslide susceptibility assessment a case study in anhua county hunan province china |
| topic | Landslide susceptibility assessment stacking ensemble machine learning extreme gradient boosting support vector classifier machine learning |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2326005 |
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