Landslide susceptibility mapping and similar case matching based on case library: a case study of Xinjing landslide, China

Landslide susceptibility mapping (LSM) predicts potential landslide risks based on historical landslides, playing a crucial role in landslide prevention and control. This study proposes a novel approach for LSM in open-pit mines, leveraging a comprehensive landslide case database. This method addresses the limitations of traditional LSM approaches, such as sensitivity to historical landslide records, low-quality inventories, and limited dataset size. By expanding the training set and incorporating case-based reasoning (CB-LSM) using Long Short-Term Memory (LSTM) for machine learning, the model demonstrated a 10% improvement in predictive accuracy compared to statistical models, showing strong applicability even in areas with low-quality landslide inventories. Additionally, this study enhances the practical utility of LSM by introducing a similar case matching method, employing Fuzzy C-Means (FCM) clustering, which links high-risk areas identified by LSM with specific landslide prevention measures. The proposed approach not only improves the accuracy of landslide predictions but also strengthens the connection between LSM results and effective landslide prevention strategies. Given the higher risks associated with landslides in open-pit mines, the proposed method significantly lowers the threshold for applying LSM in these environments, offering a valuable tool for safeguarding personnel and property.