Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping
In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/3/325 |
| _version_ | 1817992077542162432 |
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| author | Guirong Wang Xinxiang Lei Wei Chen Himan Shahabi Ataollah Shirzadi |
| author_facet | Guirong Wang Xinxiang Lei Wei Chen Himan Shahabi Ataollah Shirzadi |
| author_sort | Guirong Wang |
| collection | DOAJ |
| description | In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance. |
| first_indexed | 2024-04-14T01:21:45Z |
| format | Article |
| id | doaj.art-75930bd14ef943d9bf7ad1c56771f4ea |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-14T01:21:45Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-75930bd14ef943d9bf7ad1c56771f4ea2022-12-22T02:20:36ZengMDPI AGSymmetry2073-89942020-02-0112332510.3390/sym12030325sym12030325Hybrid Computational Intelligence Methods for Landslide Susceptibility MappingGuirong Wang0Xinxiang Lei1Wei Chen2Himan Shahabi3Ataollah Shirzadi4College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, IranDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, IranIn this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.https://www.mdpi.com/2073-8994/12/3/325landslide susceptibilitymultiboostingcredal decision treeradial basis function network |
| spellingShingle | Guirong Wang Xinxiang Lei Wei Chen Himan Shahabi Ataollah Shirzadi Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping Symmetry landslide susceptibility multiboosting credal decision tree radial basis function network |
| title | Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping |
| title_full | Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping |
| title_fullStr | Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping |
| title_full_unstemmed | Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping |
| title_short | Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping |
| title_sort | hybrid computational intelligence methods for landslide susceptibility mapping |
| topic | landslide susceptibility multiboosting credal decision tree radial basis function network |
| url | https://www.mdpi.com/2073-8994/12/3/325 |
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