A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit
Landslide susceptibility assessment is an important support for disaster identification and risk management. This study aims to analyze the application ability of machine learning hybrid models in different evaluation units. Three typical machine learning models, including random forest forest by pe...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2022.1009433/full |
| _version_ | 1828099912947990528 |
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| author | Niandong Deng Niandong Deng Yuxin Li Jianquan Ma Jianquan Ma Himan Shahabi Himan Shahabi Mazlan Hashim Gabriel de Oliveira Saman Shojae Chaeikar |
| author_facet | Niandong Deng Niandong Deng Yuxin Li Jianquan Ma Jianquan Ma Himan Shahabi Himan Shahabi Mazlan Hashim Gabriel de Oliveira Saman Shojae Chaeikar |
| author_sort | Niandong Deng |
| collection | DOAJ |
| description | Landslide susceptibility assessment is an important support for disaster identification and risk management. This study aims to analyze the application ability of machine learning hybrid models in different evaluation units. Three typical machine learning models, including random forest forest by penalizing attributes (FPA) and rotation forest were merged by random subspace algorithm. Twelve evaluation factors, including elevation, slope angle, slope aspect, roughness, rainfall, lithology, distance to rivers, distance to roads, normalized difference vegetation index, topographic wetness index, plan curvature, and profile curvature, were extracted from 155 landslides in Yaozhou District, Tongchuan City, China. Six landslide susceptibility maps were generated based on the slope units divided by curvature and 30 m resolution grid units. Multiple performance metrics showed that the RS-RF model based on slope units has excellent spatial prediction ability. At the same time, the method of slope unit division based on curvature is proved to be more suitable for the typical Loess tableland regions, which provides basis for the selection of evaluation units in landslide susceptibility assessment. |
| first_indexed | 2024-04-11T08:21:06Z |
| format | Article |
| id | doaj.art-2468b56d1e58418f963f4d6fbf6a4675 |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-11T08:21:06Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-2468b56d1e58418f963f4d6fbf6a46752022-12-22T04:34:57ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2022-11-011010.3389/fenvs.2022.10094331009433A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unitNiandong Deng0Niandong Deng1Yuxin Li2Jianquan Ma3Jianquan Ma4Himan Shahabi5Himan Shahabi6Mazlan Hashim7Gabriel de Oliveira8Saman Shojae Chaeikar9College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, ChinaKey Laboratory of Geological Processes and Mineral Resources, Northern Qinghai-Tibet Plateau, Xining, ChinaXi’an Meihang Remote Sensing Information Co., Ltd., Xi’an, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an, ChinaKey Laboratory of Geological Processes and Mineral Resources, Northern Qinghai-Tibet Plateau, Xining, ChinaDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, IranGeoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainability and Environment (RISE), Universiti Teknologi Malaysia, Johor Bahru, MalaysiaGeoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainability and Environment (RISE), Universiti Teknologi Malaysia, Johor Bahru, MalaysiaDepartment of Earth Sciences, University of South Alabama, Mobile, AL, United StatesFaculty of Information Technology, Monash University, Melbourne, VIC, AustraliaLandslide susceptibility assessment is an important support for disaster identification and risk management. This study aims to analyze the application ability of machine learning hybrid models in different evaluation units. Three typical machine learning models, including random forest forest by penalizing attributes (FPA) and rotation forest were merged by random subspace algorithm. Twelve evaluation factors, including elevation, slope angle, slope aspect, roughness, rainfall, lithology, distance to rivers, distance to roads, normalized difference vegetation index, topographic wetness index, plan curvature, and profile curvature, were extracted from 155 landslides in Yaozhou District, Tongchuan City, China. Six landslide susceptibility maps were generated based on the slope units divided by curvature and 30 m resolution grid units. Multiple performance metrics showed that the RS-RF model based on slope units has excellent spatial prediction ability. At the same time, the method of slope unit division based on curvature is proved to be more suitable for the typical Loess tableland regions, which provides basis for the selection of evaluation units in landslide susceptibility assessment.https://www.frontiersin.org/articles/10.3389/fenvs.2022.1009433/fulllandslide susceptibilitymachine learninghybrid modelslope unitgrid unit |
| spellingShingle | Niandong Deng Niandong Deng Yuxin Li Jianquan Ma Jianquan Ma Himan Shahabi Himan Shahabi Mazlan Hashim Gabriel de Oliveira Saman Shojae Chaeikar A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit Frontiers in Environmental Science landslide susceptibility machine learning hybrid model slope unit grid unit |
| title | A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit |
| title_full | A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit |
| title_fullStr | A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit |
| title_full_unstemmed | A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit |
| title_short | A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit |
| title_sort | comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit |
| topic | landslide susceptibility machine learning hybrid model slope unit grid unit |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2022.1009433/full |
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