A Model for Complementing Landslide Types (Cliff Type) Missing from Areal Disaster Inventories Based on Landslide Conditioning Factors for Earthquake-Proof Regions

Precise classification of landslide types is critical for targeted hazard mitigation, although the absence of type-specific classifications in many existing inventories limits their utility for effective risk management. This study develops a transferable machine learning approach to identify cliff-type landslides from unclassified records, with a focus on earthquake-prone regions. Using the Forest-based and Boosted Classification and Regression (FBCR) tools in ArcGIS Pro, a model was trained on 167 landslide points and 167 non-landslide points from Tokushima Prefecture, Japan. The model achieved high predictive performance, with 84% accuracy and sensitivity, an F1 score of 84%, and a Matthews correlation coefficient (MCC) of 0.68. The trained model was applied to the Kegalle District, Sri Lanka, and validated against a recently updated inventory specifying landslide types, resulting in an accuracy of 80.1%. It also enabled retrospective identification of cliff-type landslides in older inventories, providing valuable insights for early hazard assessment. Spatial analysis showed strong correspondence between predicted cliff-type zones and key conditioning factors, including specific elevation ranges, steep slopes, high soil thickness, and proximity to roads and buildings. This study integrates FBCR-based modelling with a cross-regional application framework for cliff-type landslide classification, offering a practical, transferable tool for refining inventories, guiding countermeasures, and improving preparedness in regions with similar geomorphological and seismic settings.