Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques
Data-driven landslide susceptibility assessment (LSA) remains unconvincing owing to the disconnection from the modelling to physical cognition of landslide causation. Most models are mere good fits to certain datasets and can produce unexpected bias in their prediction, misleading high-risk area zon...
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2509857 |
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| author | Miao Yu Li Chen Xuanmei Fan Peifeng Ma Shaojie Zhao Zhongwang Wu Fan Yang Longwei Xu Monan Shan Xiao Xie Haowei Zeng Zhengjia Zhang |
| author_facet | Miao Yu Li Chen Xuanmei Fan Peifeng Ma Shaojie Zhao Zhongwang Wu Fan Yang Longwei Xu Monan Shan Xiao Xie Haowei Zeng Zhengjia Zhang |
| author_sort | Miao Yu |
| collection | DOAJ |
| description | Data-driven landslide susceptibility assessment (LSA) remains unconvincing owing to the disconnection from the modelling to physical cognition of landslide causation. Most models are mere good fits to certain datasets and can produce unexpected bias in their prediction, misleading high-risk area zoning. To validate data-driven LSA, this study delved into the innate interactions between input landslide features and predictions by model explanation and compared feature permutation results with landslide statistical priors. Furthermore, multi-temporal interferometric synthetic aperture radar (MT-InSAR) derived ground deformation was applied in an alpha pixel fusion and growth method for LSA enhancement. This study took Hong Kong as the research area, employed extreme gradient boosting (XGBoost) for LSA, and utilized Shapley additive explanations (SHAP) method for model explanation. The mean Shapley values – indicating feature importance – for slope, stream power index (SPI) and land use are 1.31, 1.12, and 0.67, respectively. This aligns with the landslide feature permutation derived from prior statistics, verifying the model prediction reliability. The applied InSAR enhancement results in a 13% increase of ‘(very) high’ landslide susceptibility areas. Additionally, LSA results and ground deformation map were cross-validated in the virtual geographic environment. This study improves the reliability of data-driven LSA through model explanation and InSAR enhancement. |
| format | Article |
| id | doaj-art-25d8fda33dde478e954f4099bb366c92 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-25d8fda33dde478e954f4099bb366c922025-08-25T11:31:38ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2509857Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniquesMiao Yu0Li Chen1Xuanmei Fan2Peifeng Ma3Shaojie Zhao4Zhongwang Wu5Fan Yang6Longwei Xu7Monan Shan8Xiao Xie9Haowei Zeng10Zhengjia Zhang11Department of Space Information, Space Engineering University, Beijing, People’s Republic of ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, People’s Republic of ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, People’s Republic of ChinaInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, People’s Republic of ChinaDepartment of Space Information, Space Engineering University, Beijing, People’s Republic of ChinaDepartment of Space Information, Space Engineering University, Beijing, People’s Republic of ChinaDepartment of Space Information, Space Engineering University, Beijing, People’s Republic of ChinaDepartment of Space Information, Space Engineering University, Beijing, People’s Republic of ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, People’s Republic of ChinaInstitute of Applied Ecology, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaGeomatics Center, Chengdu Institute of Survey & Investigation, Chengdu, People’s Republic of ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, People’s Republic of ChinaData-driven landslide susceptibility assessment (LSA) remains unconvincing owing to the disconnection from the modelling to physical cognition of landslide causation. Most models are mere good fits to certain datasets and can produce unexpected bias in their prediction, misleading high-risk area zoning. To validate data-driven LSA, this study delved into the innate interactions between input landslide features and predictions by model explanation and compared feature permutation results with landslide statistical priors. Furthermore, multi-temporal interferometric synthetic aperture radar (MT-InSAR) derived ground deformation was applied in an alpha pixel fusion and growth method for LSA enhancement. This study took Hong Kong as the research area, employed extreme gradient boosting (XGBoost) for LSA, and utilized Shapley additive explanations (SHAP) method for model explanation. The mean Shapley values – indicating feature importance – for slope, stream power index (SPI) and land use are 1.31, 1.12, and 0.67, respectively. This aligns with the landslide feature permutation derived from prior statistics, verifying the model prediction reliability. The applied InSAR enhancement results in a 13% increase of ‘(very) high’ landslide susceptibility areas. Additionally, LSA results and ground deformation map were cross-validated in the virtual geographic environment. This study improves the reliability of data-driven LSA through model explanation and InSAR enhancement.https://www.tandfonline.com/doi/10.1080/17538947.2025.2509857Landslide susceptibilitymachine learningInSARmodel explanation |
| spellingShingle | Miao Yu Li Chen Xuanmei Fan Peifeng Ma Shaojie Zhao Zhongwang Wu Fan Yang Longwei Xu Monan Shan Xiao Xie Haowei Zeng Zhengjia Zhang Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques International Journal of Digital Earth Landslide susceptibility machine learning InSAR model explanation |
| title | Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques |
| title_full | Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques |
| title_fullStr | Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques |
| title_full_unstemmed | Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques |
| title_short | Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques |
| title_sort | validating and enhancing data driven landslide susceptibility prediction by model explanation and mt insar techniques |
| topic | Landslide susceptibility machine learning InSAR model explanation |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2509857 |
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