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

Sushama De Silva () and Uchimura Taro
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Sushama De Silva: Department of Civil and Environmental Engineering, Saitama University, 255 Shimookubo, Sakura Ward, Saitama-shi 338-8570, Japan
Uchimura Taro: Department of Civil and Environmental Engineering, Saitama University, 255 Shimookubo, Sakura Ward, Saitama-shi 338-8570, Japan

Sustainability, 2025, vol. 17, issue 17, 1-45

Abstract: 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.

Keywords: cliff type; landslide (LS); inventory; landslide conditioning factors (LCF); Forest-based and Boosted Classification and Regression tool (FBCR); model; Tokushima Prefecture (TP); Kegalle District (KD) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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