Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Abstract

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding and rely on relatively static environmental conditions, which exposes the hysteresis of landslide susceptibility assessment in refined-scale and temporal dynamic changes. This study presents an improved landslide susceptibility assessment approach by integrating machine learning models based on random forest (RF), logical regression (LR), and gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology and comparing them to their respective original models. The results demonstrated that the combined approach improves prediction accuracy and reduces the false negative and false positive errors. The LR-InSAR model showed the best performance in dynamic landslide susceptibility assessment at both regional and smaller scale, particularly when identifying areas of high and very high susceptibility. Modeling results were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. This study is of great significance to accurately assess dynamic landslide susceptibility and to help reduce and prevent landslide risk.

Keywords

Landslide susceptibility / Machine learning models / InSAR / Dynamic assessment

CRediT authorship contribution statement

Yingdong Wei: Writing – original draft, Visualization, Methodology, Formal analysis. Haijun Qiu: Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. Zijing Liu: Methodology, Investigation. Wenchao Huangfu: Validation, Resources. Yaru Zhu: Resources, Data curation. Ya Liu: Resources, Methodology. Dongdong Yang: Visualization, Validation. Ulrich Kamp: Writing – review & editing, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was funded by the National Natural Science Foundation of China (Grant No. 42271078), and Key Research and Development Program of Shaanxi (2024SF-YBXM-669).

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