Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms
Optimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. Xiaojin County in China has complex geology and active hazards, posing a threat to human and economic security. This study evaluated landslide susceptibility...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2507919 |
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| _version_ | 1849729915238219776 |
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| author | Xia Zhao Wei Chen Paraskevas Tsangaratos Ioanna Ilia Qingfeng He |
| author_facet | Xia Zhao Wei Chen Paraskevas Tsangaratos Ioanna Ilia Qingfeng He |
| author_sort | Xia Zhao |
| collection | DOAJ |
| description | Optimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. Xiaojin County in China has complex geology and active hazards, posing a threat to human and economic security. This study evaluated landslide susceptibility in Xiaojin County with high terrain heterogeneity by comparing three data-driven models - WoE, optimized RF, and RBFNetworks. The spatial correlation between 12 conditioning factors and landslides was analyzed, and the multicollinearity between factors was determined, and the significance of the factors was quantified by MDA and MDG, especially for elevation, soil, and distance to roads. The WoE model exhibits exceptional performance (AUC: 0.899 for training, 0.892 for validation), outperforming RF (AUC: 0.880 for training, 0.874 for validation) and RBFNetwork (AUC: 0.866 for training, 0.863 for validation). The results have significant implications for land development and management in Xiaojin County, while also challenging the machine learning paradigm. |
| format | Article |
| id | doaj-art-62d0a79a9e8b49f696292c0fa324f558 |
| institution | DOAJ |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-62d0a79a9e8b49f696292c0fa324f5582025-08-20T03:09:02ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2507919Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithmsXia Zhao0Wei Chen1Paraskevas Tsangaratos2Ioanna Ilia3Qingfeng He4College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an, ChinaLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou, GreeceCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an, ChinaOptimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. Xiaojin County in China has complex geology and active hazards, posing a threat to human and economic security. This study evaluated landslide susceptibility in Xiaojin County with high terrain heterogeneity by comparing three data-driven models - WoE, optimized RF, and RBFNetworks. The spatial correlation between 12 conditioning factors and landslides was analyzed, and the multicollinearity between factors was determined, and the significance of the factors was quantified by MDA and MDG, especially for elevation, soil, and distance to roads. The WoE model exhibits exceptional performance (AUC: 0.899 for training, 0.892 for validation), outperforming RF (AUC: 0.880 for training, 0.874 for validation) and RBFNetwork (AUC: 0.866 for training, 0.863 for validation). The results have significant implications for land development and management in Xiaojin County, while also challenging the machine learning paradigm.https://www.tandfonline.com/doi/10.1080/10106049.2025.2507919Landslide susceptibility modelingweights-of-evidencerandom forestradial basis function networkGIS |
| spellingShingle | Xia Zhao Wei Chen Paraskevas Tsangaratos Ioanna Ilia Qingfeng He Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms Geocarto International Landslide susceptibility modeling weights-of-evidence random forest radial basis function network GIS |
| title | Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms |
| title_full | Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms |
| title_fullStr | Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms |
| title_full_unstemmed | Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms |
| title_short | Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms |
| title_sort | landslide spatial prediction using data driven based statistical and hybrid computational intelligence algorithms |
| topic | Landslide susceptibility modeling weights-of-evidence random forest radial basis function network GIS |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2507919 |
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