Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorKorea University-
Autor(es): dc.creatorAlcântara, Enner-
Autor(es): dc.creatorBaião, Cheila Flávia-
Autor(es): dc.creatorGuimarães, Yasmim Carvalho-
Autor(es): dc.creatorMantovani, José Roberto-
Autor(es): dc.creatorMarengo, José Antonio-
Data de aceite: dc.date.accessioned2025-08-21T15:55:17Z-
Data de disponibilização: dc.date.available2025-08-21T15:55:17Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.nhres.2024.10.003-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308873-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308873-
Descrição: dc.descriptionThis study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (Unesp), SP-
Descrição: dc.descriptionGraduate Program in Natural Disasters (Unesp/CEMADEN), SP-
Descrição: dc.descriptionGraduate School of International Studies Korea University-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (Unesp), SP-
Descrição: dc.descriptionGraduate Program in Natural Disasters (Unesp/CEMADEN), SP-
Idioma: dc.languageen-
Relação: dc.relationNatural Hazards Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBrazil-
Palavras-chave: dc.subjectLand use change-
Palavras-chave: dc.subjectLandslide susceptibility-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectSão sebastião-
Título: dc.titleMachine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.