Examining the Influence of Different Inventories on Shallow Landslide Susceptibility Modeling: An Assessment Using Machine Learning and Statistical Approaches

Author(s)

Dias, Helen Cristina ; Hoelbling, Daniel ; Grohmann, Carlos Henrique

• Document type: Journal article
• Source: GEOSCIENCES; v. 15, n. 3, p. 17-pg., 2025-02-20.

Abstract

Shallow landslides are one of the most common natural hazards in Brazil and worldwide. Susceptibility maps are powerful tools to analyze the spatial probability of shallow landslide occurrences. The outputs of susceptibility maps strongly depend on the type of landslide inventory used. The aim of this study is to examine the influence of different inventories on shallow landslide susceptibility modeling using the different methods LR, SVM, and XGBoost. Three different shallow landslide inventories were compiled following a single extreme rainfall event in the Ribeira Valley, S & atilde;o Paulo, Brazil. The results indicate that inventories generated through different landslide detection methods and imagery produce diverse susceptibility maps, as evidenced by the calculated Cohen's Kappa coefficient values (0.33-0.79). The agreement among the models varied depending on the specific model: LR exhibited the highest agreement (0.79), whereas SVM (0.36) and XGBoost (0.33) showed lower numbers. Conversely, the accuracy numbers suggest that XGBoost achieved the highest success rate in terms of AUC (85-78%), followed by SVM (82-76%), and LR (80-71%). Inventories obtained through different detection methods, using distinct datasets, can directly influence the susceptibility assessment, leading to varying classifications of the same area. These findings demonstrate the importance of well-established landslide mapping criteria. (AU)

• FAPESP's process: 22/01534-8 - Creation and assessment of shallow landslides inventories based on the Object-Based Image Analysis (OBIA) method
• Grantee: Helen Cristina Dias
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 23/11197-1 - Multi-Scale Geomorphometric Analysis of Mass Movements in São Sebastião (SP, Brazil)
• Grantee: Carlos Henrique Grohmann de Carvalho
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/26568-0 - High-resolution remote sensing, deep learning and geomorphometry in analyses of mass movements and geological risk
• Grantee: Carlos Henrique Grohmann de Carvalho
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/17261-8 - Analysis of manual and semi-automatic shallow landslides inventories and its suitability in predictive models
• Grantee: Helen Cristina Dias
• Support Opportunities: Scholarships in Brazil - Doctorate