Landslide Susceptibility Assessment in the Brazilian Atlantic Forest 2020

Published: 25 March 2024| Version 1 | DOI: 10.17632/szyvpp896k.1
Caio Azevedo


The research employs various datasets to develop a susceptibility map for shallow landslides in Guarujá, a significant city within the Brazilian Atlantic Forest biome. Morphometric features extracted through ArcGIS software, such as slope, hillside curvature, topographic moisture index, and aspect, were derived from a digital terrain model (DTM). Geological data sourced from the Geological Map of São Paulo and land use data from the Environmental Planning Office of São Paulo complement these morphometric features. Additionally, the scar inventory of landslides documented by the Municipal Coordinator of Protection and Civil Defense of Guarujá for 2020 was utilized. To ensure effective model training, both occurrence (landslide) and nonoccurrence (not prone to landslide) samples were acquired. Nonoccurrence data were generated employing three methodologies: random distribution, point generation within specified radii around occurrence points, and buffer creation around landslide scars. This comprehensive approach to dataset selection and generation aims to enhance the accuracy and generalization ability of the landslide susceptibility model.



Universidade de Sao Paulo Escola Politecnica, Universidade de Sao Paulo, Universidade do Porto, Instituto de Pesquisas Tecnologicas


Artificial Neural Network, Machine Learning, Landslide, Neural Network