Characteristics of Landslides Induced by Typhoon “Gaemi” in Zixing City， Hunan Province， July 2024， and Their Geological Control Factors

LIU Ruiyang; XU Qiang; PU Chuanhao; XU Fanshu; WANG Xiaochen; ZHAO Haoxing; ZHU Xing; HE Na

doi:10.13203/j.whugis20250027

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Characteristics of Landslides Induced by Typhoon “Gaemi” in Zixing City， Hunan Province， July 2024， and Their Geological Control Factors

Remote Sensing and Geological Disasters | 更新时间：2026-11-05

- Characteristics of Landslides Induced by Typhoon “Gaemi” in Zixing City， Hunan Province， July 2024， and Their Geological Control Factors
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 7, Pages: 1452-1469(2026)
- 作者机构：
  
  1.成都理工大学地质灾害防治与地质环境保护全国重点实验室，四川成都，610059
  2.广西壮族自治区地质环境监测站，广西南宁，530201
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250027
  CLC： P237
- Received：12 September 2025，
  
  Published：05 July 2026
- 稿件说明：
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刘瑞阳,许强,蒲川豪，等.湖南省资兴市2024年7月台风“格美”诱发滑坡特征及其地质控制作用[J].武汉大学学报（信息科学版）,2026,51(7):1452-1469.

LIU Ruiyang,XU Qiang,PU Chuanhao,et al.Characteristics of Landslides Induced by Typhoon “Gaemi” in Zixing City, Hunan Province, July 2024, and Their Geological Control Factors[J].Geomatics and Information Science of Wuhan University,2026,51(7):1452-1469.
刘瑞阳,许强,蒲川豪，等.湖南省资兴市2024年7月台风“格美”诱发滑坡特征及其地质控制作用[J].武汉大学学报（信息科学版）,2026,51(7):1452-1469. DOI：10.13203/j.whugis20250027

LIU Ruiyang,XU Qiang,PU Chuanhao,et al.Characteristics of Landslides Induced by Typhoon “Gaemi” in Zixing City, Hunan Province, July 2024, and Their Geological Control Factors[J].Geomatics and Information Science of Wuhan University,2026,51(7):1452-1469. DOI：10.13203/j.whugis20250027

摘要

2024⁃07⁃26，台风“格美”袭击中国湖南省资兴市，诱发了大规模滑坡灾害，造成房屋损毁和多处道路阻断，并导致严重人员伤亡。目前，对于台风“格美”诱发滑坡的发育分布与形成条件仍不清晰，影响灾后重建和风险隐患排查。基于灾后遥感影像，采用深度学习结合人工校核建立滑坡数据库，分析孕灾环境下滑坡的分布规律；采用可解释性机器学习进行易发性建模，探讨地质条件对滑坡形成的控制作用。结果表明，台风“格美”在资兴市共诱发约16 120处滑坡，总面积约32.16 km²，主要分布于海拔200~600 m、坡度20°~30°的斜坡上，最大点密度达129个/km²，群发性效应显著。滑坡主要受地质条件控制，其中63.5%的滑坡分布在花岗岩区域，29.4%分布在砂岩及其浅变质岩区域。不同岩性区域，滑坡的主控因子具有显著差异。

Abstract

Objectives

On July 26

2024

Typhoon “Gaemi” struck Zixing City

Hunan Province

China

triggering large-scale landslide disasters that caused extensive damage to buildings

blocked several roads

and resulted in significant casualties. However

the development

distribution

and formation conditions of the landslides triggered by Typhoon “Gaemi” remain unclear

which impacts post-disaster reconstruction and risk assessment.

Methods

We present the identification and analysis of landslides induced by Typhoon “Gaemi” through three main parts: landslide recognition

spatial and statistical analysis

and explainable machine learning. First

historical rainfall-induced landslide data from regions like Longxi

Yan'an

and Shaoguan are used to train and optimize the YOLOv8 model for improved accuracy and robustness. The model is applied to post-disaster optical remote sensing imagery for automatic identification

with manual verification and the creation of a landslide database for Zixing City. Next

spatial distribution characteristics of the landslides are analyzed using environmental data

revealing the spatial patterns and controlling factors of landslides induced by Typhoon “Gaemi”. Finally

statistical data are used to train a machine learning model to assess landslide susceptibility

with Shapley additive explanations (SHAP) employed to interpret the model and identify key factors influencing landslide occurrence. These methods provide a comprehensive analysis of landslide risks and serve as a basis for disaster prevention.

Results

Using the deep learning model YOLOv8 and manual verification

we identify approximately 16 120 landslides triggered by Typhoon “Gaemi” in Zixing City

covering a total area of 32.16 km². These landslides are predominantly concentrated in the eastern mountainous areas of Zixing City

with a peak point density of 129 per km²

exhibiting a notable clustering effect. Statistical analysis reveals that most of the landslides occur on slopes with an elevation range of 200⁃600 m and a slope gradient of 20°⁃30°. Further analysis demonstrates that the occurrence of these landslides is significantly influenced by geological conditions

with 63.5% of landslides occurring in granite regions and 29.4% in sandstone and shallow metamorphic rock areas. Random forest modeling and SHAP analysis identify lithology as the primary factor controlling landslide formation. The importance of disaster-triggering factors varies across different lithological regions. In granite areas

faults are the most significant factor influencing landslide occurrence

while human activities also contribute notably to landslide formation. In sandstone regions

river erosion is the dominant factor controlling landslide formation.

Conclusions

The deep learning and machine learning models used in this study demonstrate clear advantages in landslide identification

susceptibility assessment

and analysis of disaster-triggering factors. The identified distribution patterns of landslides and the geological control mechanisms can provide scientific evidence for analyzing the landslide formation conditions and disaster prevention and control in Zixing City and similar regions with comparable geological conditions.

关键词

Keywords

references

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