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...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2509857 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1753-8947 1753-8955 |