Validating and enhancing data-driven landslide susceptibility prediction by model explanation and MT-InSAR techniques

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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Bibliographic Details
Main Authors: Miao Yu, Li Chen, Xuanmei Fan, Peifeng Ma, Shaojie Zhao, Zhongwang Wu, Fan Yang, Longwei Xu, Monan Shan, Xiao Xie, Haowei Zeng, Zhengjia Zhang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:International Journal of Digital Earth
Subjects:
Landslide susceptibility
machine learning
InSAR
model explanation
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2509857
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https://www.tandfonline.com/doi/10.1080/17538947.2025.2509857

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