A comprehensive review of key issues in landslide susceptibility prediction and their solutions using semi-supervised imbalanced theory

作者：Faming Huang, Yang Yang, Shui‐Hua Jiang, Chuangbing ZHOU, Xuanmei Fan, Lihan Pan, Chi Yao, Haowen Xiong, Zhilu Chang · 发表于：Chinese journal of rock mechanics and engineering · 年份：2025 · DOI：10.3724/1000-6915.jrme.2024.1003 · 被引用次数：1 · 研究领域：Landslides and related hazards、Rock Mechanics and Modeling、Synthetic Aperture Radar (SAR) Applications and Techniques

As the foundation for regional landslide risk assessment, landslide susceptibility prediction (LSP) is a prominent and challenging topic in global landslide disaster prevention and control research. This paper systematically reviews critical issues in LSP, including model selection, identification and integration of conditioning factors, determination and classification of prediction units, methods for selecting non-landslide samples, optimization of the landslide-to-non-landslide sample ratio, assignment of label values to non-landslide samples, and evaluation methodologies for landslide susceptibility results. The literature review indicates that tree-based models, such as Decision Trees and Random Forests, demonstrate superior performance. The selection and integration of conditioning factors should adhere to principles of comprehensive typology and clear physical significance. Prediction units can be defined through multi-scale segmentation of slope units. Non-landslide samples should preferably be randomly selected from areas characterized by very low or low susceptibility. The optimal ratio of landslide to non-landslide samples can be established through experiments under various conditions. Specific small probability values should be assigned to non-landslide samples. Evaluation of LSP results necessitates a comprehensive consideration of multiple metrics, including ROC accuracy, prediction rate accuracy, and the mean and standard deviation of the susceptibility index....