Improving Landslide Susceptibility Assessment Through Non-Landslide Sampling Strategies

Liping Tu, Meiqiu Chen (), Peng Leng, Shengwei Liu, Mei’e Liu, Wang Luo and Yaqin Mao
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Liping Tu: College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China
Meiqiu Chen: College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China
Peng Leng: Jiangxi Nuclear Industry Surveying and Mapping Institute Group Co., Ltd., Nanchang 330038, China
Shengwei Liu: Jiangxi Nuclear Industry Surveying and Mapping Institute Group Co., Ltd., Nanchang 330038, China
Mei’e Liu: Jiangxi Provincial Nuclear Industry Geological Survey Institute, Nanchang 330038, China
Wang Luo: Jiangxi Nuclear Industry Surveying and Mapping Institute Group Co., Ltd., Nanchang 330038, China
Yaqin Mao: Jiangxi Provincial Nuclear Industry Geological Survey Institute, Nanchang 330038, China

Land, 2025, vol. 14, issue 10, 1-27

Abstract: Landslides are a prevalent geological hazard in China, posing significant threats to life and property. Landslide susceptibility assessment is essential for disaster prevention, and the quality of non-landslide samples critically affects model accuracy. This study takes Yongxin County, Jiangxi Province, as a case, selecting ten susceptibility factors and applying the Random Forest (RF) model with six non-landslide sampling methods for comparison. Results indicate that non-landslide sample selection substantially influences model performance, with the RF model using the IV method achieving the highest accuracy (AUC = 0.9878). SHAP analysis identifies NDVI, slope, lithology, land cover, and elevation as the primary contributing factors. Statistical results show that RF_IV non-landslide sample predictions are lowest, mainly below 0.18, with a median of 0.18, confirming that the IV method effectively excludes landslide-prone areas and accurately represents non-landslide regions. These findings provide practical guidance for landslide risk managers, local authorities, and policymakers, and offer methodological insights for researchers in geological hazard modeling.

Keywords: non-landslide samples; sampling methods; landslide; susceptibility assessment; random forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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