Analysis of the influence of topography parameters on landslide susceptibility using machine learning: A case study in the municipality of São Sebastião - SP

• Grant number: 24/18298-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): December 01, 2024
• Effective date (End): November 30, 2025
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geology
• Principal Investigator: Camila Duelis Viana
• Grantee: Beatriz Azevedo Ferreira
• Host Institution: Instituto de Geociências (IGC). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 23/11197-1 - Multi-Scale Geomorphometric Analysis of Mass Movements in São Sebastião (SP, Brazil), AP.R

Abstract

Landslides are recurrent events in Brazil, causing significant losses. Landslide susceptibility modeling (LSM) models the probability of occurrence of such events based on the analysis of their conditioning and triggering factors, and is an important tool in risk and disaster management. Machine learning techniques have been increasingly used in this context, especially for their objectivity and effectiveness. However, the study of the influence of topography parameters, derived from digital elevation models at different scales, is still incipient, as is the importance of each factor for LSM. The proposal of this Scientific Initiation project is to provide the student with applied research on landslide susceptibility in Serra do Mar, in the São Sebastião region, through remote sensing and the use of machine learning techniques, in order to analyze the influence of different topography parameters, originating from digital terrain models at different scales, in the modeling of landslide susceptibility by machine learning. This project is associated to the Regular Research Project entitled "Multi-scale geomorphometry in the analysis of mass movements in São Sebastião (SP)" (FAPESP process 2023/11197-1).