Assessing Landslide Susceptibility Using One‐Class Classification and SHAP : A Case Study of Luding County, China

ABSTRACT Current machine learning‐based landslide susceptibility assessment heavily relies on supervised classification, which necessitates both landslide and non‐landslide samples. However, the selection of non‐landslide samples (negative samples) suffers from significant epistemic uncertainty and a lack of standardized criteria, introducing bias that compromises model reliability. To bridge this gap, this study proposes a novel framework using One‐Class Classification (OCC), which eliminates the dependency on unreliable negative samples by training exclusively on landslide occurrences. We utilize a historical landslide dataset from Luding County, China, prior to the earthquake on September 5, 2022 as the training data, and post‐earthquake landslide data as the testing data. We model the data using three one‐class classifiers: One‐Class Support Vector Machines (OCSVM), Isolation Forest (IForest), and One‐Class K‐nearest neighbors (OCKNN). Then we compare the results with supervised learning classification algorithms based on Support Vector Machines (SVM), Random Forest, and KNN. The results show that OCSVM has a higher recall rate (0.865) than SVM (0.639) for high susceptibility areas. IForest has a higher recall rate (0.903) than RandomForest (0.884). OCKNN performs the best with a recall rate of 0.968, surpassing KNN classification (0.923). Furthermore, we employ SHAP to interpret the OCKNN model, identifying elevation as the most influential factor in landslide susceptibility, followed by TRI and slope. This enhances the interpretability of the model and provides insights into the driving factors of landslides. The results demonstrate that the proposed one‐class classification effectively addresses the issue of negative sample quality in traditional supervised learning. This study provides a new approach for landslide susceptibility assessment in data‐scarce regions.