Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China
Reliable landslide susceptibility maps are essential for geohazard risk management. However, the selection of models and methods in the landslide susceptibility modeling (LSM) process is subjective and can lead to uncertainties in susceptibility outcomes. This paper introduces a framework for LSM an...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2344804 |
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| author | Liang Liu Jiqiu Deng |
| author_facet | Liang Liu Jiqiu Deng |
| author_sort | Liang Liu |
| collection | DOAJ |
| description | Reliable landslide susceptibility maps are essential for geohazard risk management. However, the selection of models and methods in the landslide susceptibility modeling (LSM) process is subjective and can lead to uncertainties in susceptibility outcomes. This paper introduces a framework for LSM and uncertainty analysis that leverages multiple models in key steps. The framework dynamically operates and combines models at each LSM step to generate susceptibility evaluation results from various method combinations. The uncertainty of different model combinations is then analyzed by comparing these results. The framework’s effectiveness is validated using Baoji City as a case study, examining the impact of different attribute interval numbers (AIN), non-landslide negative sample selection methods, and prediction models on susceptibility prediction outcomes. The results of each group show that the highest AUC value is 0.963 (AIN = 12, buffer-controlled sampling, Random forest) is about 0.26 higher than the lowest (no AIN, low-slope controlled sampling, Support vector machine). The findings suggest that the combinations involving larger AIN values, buffer-controlled sampling, XGBoost, or Random forest model yield relatively high AUC accuracy and relatively low uncertainty in the susceptibility index. |
| format | Article |
| id | doaj-art-c2beb0a83d8a49199e7a78248638de04 |
| institution | Kabale University |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-c2beb0a83d8a49199e7a78248638de042024-12-12T18:11:16ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2344804Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, ChinaLiang Liu0Jiqiu Deng1Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha, ChinaReliable landslide susceptibility maps are essential for geohazard risk management. However, the selection of models and methods in the landslide susceptibility modeling (LSM) process is subjective and can lead to uncertainties in susceptibility outcomes. This paper introduces a framework for LSM and uncertainty analysis that leverages multiple models in key steps. The framework dynamically operates and combines models at each LSM step to generate susceptibility evaluation results from various method combinations. The uncertainty of different model combinations is then analyzed by comparing these results. The framework’s effectiveness is validated using Baoji City as a case study, examining the impact of different attribute interval numbers (AIN), non-landslide negative sample selection methods, and prediction models on susceptibility prediction outcomes. The results of each group show that the highest AUC value is 0.963 (AIN = 12, buffer-controlled sampling, Random forest) is about 0.26 higher than the lowest (no AIN, low-slope controlled sampling, Support vector machine). The findings suggest that the combinations involving larger AIN values, buffer-controlled sampling, XGBoost, or Random forest model yield relatively high AUC accuracy and relatively low uncertainty in the susceptibility index.https://www.tandfonline.com/doi/10.1080/19475705.2024.2344804Landslide susceptibility mappinguncertainty analysismachine learningnon-landslide sampleattribute interval number |
| spellingShingle | Liang Liu Jiqiu Deng Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China Geomatics, Natural Hazards & Risk Landslide susceptibility mapping uncertainty analysis machine learning non-landslide sample attribute interval number |
| title | Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China |
| title_full | Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China |
| title_fullStr | Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China |
| title_full_unstemmed | Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China |
| title_short | Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China |
| title_sort | multi model combination in key steps for landslide susceptibility modeling and uncertainty analysis a case study in baoji city china |
| topic | Landslide susceptibility mapping uncertainty analysis machine learning non-landslide sample attribute interval number |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2344804 |
| work_keys_str_mv | AT liangliu multimodelcombinationinkeystepsforlandslidesusceptibilitymodelinganduncertaintyanalysisacasestudyinbaojicitychina AT jiqiudeng multimodelcombinationinkeystepsforlandslidesusceptibilitymodelinganduncertaintyanalysisacasestudyinbaojicitychina |