| Citation: | QI Lining，GUO Chaoxu，MENG Huajun，et al. Recognition of rainfall-induced landslide in high vegetation coverage area based on machine learning[J]. Bulletin of Geological Science and Technology，2026，45（1）：185-198 doi: 10.19509/j.cnki.dzkq.tb20240298
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In recent years, influenced by global warming, the southeast coastal provinces of China have frequently experienced super typhoons, heavy rainfall, and prolonged monsoon rains, triggering numerous shallow landslides the pose serious threats to local lives and property.
To rapidly obtain more accurate disaster information for support emergency response and scientific decision-making,
this study developed an automatic recognition model for shallow landslides using a machine learning approach, the LightGBM decision tree algorithm. The model targets clustered shallow landslides developed in highly weathered granite layers induced by heavy rainfall in high vegetation coverage area. High-resolution aerial images and DOM data from Wuping County, Fujian Province, were used, with feature parameters including slope, elevation, plane curvature (
The results indicate that: ①The multi-feature combination model achieves higher accuracy in landslide recognition compared to single-feature models; ②The rapid recognition model incorporating slope, elevation, plane curvature, profile curvature, and R, G, and B bands accurately identifies existing landslides, with both
This research provides a novel approach for early recognition of rainfall-induced landslides in high vegetation coverage area and offers scientific and technological support for risk prevention and disaster response decision-making in typhoon-related and rainstorm-affected areas such as Haixi.
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