Identificação de áreas vulneráveis a movimentos de massa com base em aprendizado de máquina, Rio Ligeiro (Pato Branco – PR)

• Autor(a) principal: Pontes, Priscila da Silva Victorino
• Data de Publicação: 2023
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: https://tede.unioeste.br/handle/tede/6920
• topic: Estabilidade de encosta; Aprendizado de máquina; Movimento de massa; Slope stability; Machine learning; Landslides; CIÊNCIAS HUMANAS:GEOGRAFIA

Studies involving the occurrence of landslides are still infrequent in municipalities in the Southwest of Paraná, which had a low population rate until the 1970s. However, this region has demonstrated a significant urbanization growth in recent decades, mainly in its hubs, such as Francisco Beltrão, Palmas, and Pato Branco. The relief of the Southwest mesoregion is marked by morphological homogeneity resulting from the predominance of flat, wavy features and a portion of strongly undulating areas. Thus, given the recurrent landslides in places of urban growth and the particular characteristics of the Southwest of Paraná relief , this work seeks to apply a methodology for identifying susceptible and vulnerable areas to landslides. A pilot experiment was carried out in the municipality of Pato Branco – PR at the limit of the Ligeiro River basin. Possible natural and anthropogenic conditions causing landslides were identified and the machine learning technique was applied to discover patterns of occurrence between these conditions. Random Forest (FR), LibSVM, and LazyKStar algorithms were used for data modeling and validation. From the results obtained by the machine learning technique, criterionmaps were generated representing the possible areas susceptible to landslides in the region of interest