Assessment of susceptibility to landslides through geographic information systems and the logistic regression model

Roberta Plangg Riegel, Darlan Daniel Alves, Bruna Caroline Schmidt, Guilherme Garcia Oliveira, Claus Haetinger, Daniela Montanari Migliavacca Osório, Marco Antônio Siqueira Rodrigues and Daniela Muller Quevedo ()
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Roberta Plangg Riegel: Feevale University
Darlan Daniel Alves: Feevale University
Bruna Caroline Schmidt: Feevale University
Guilherme Garcia Oliveira: Federal University of Rio Grande Do Sul
Claus Haetinger: Vale do Taquari University – Univates
Daniela Montanari Migliavacca Osório: Feevale University
Marco Antônio Siqueira Rodrigues: Feevale University
Daniela Muller Quevedo: Feevale University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 103, issue 1, No 23, 497-511

Abstract: Abstract The increase in the frequency of natural disasters in recent years and its consequent social, economic and environmental impacts make it possible to prioritize areas of risk as an essential measure in order to maximize harm reduction. This case study, developed in the city of Novo Hamburgo, Rio Grande do Sul state, Brazil, aims to identify and evaluate areas susceptible to mass movements, through the development of a model based on logistic regression, associated to Geographic Information System (GIS). The construction of the model was based on the use of only four independent variables (slope, geological aspects, pedological aspects and land use and coverage) and a binary variable, which refers to the occurrence of mass movements. In total, 123,308 pixels were used as samples for the logistic regression modeling in SPSS software. As a result, we have the spatialization of a mass movement probability map with 87.3% of the correctly sorted pixels. A validation with the landslide susceptibility map built by the Brazilian Geological Survey was also performed using the receiver operating characteristic (ROC) curve, indicating a prediction accuracy of 82.5%. This research showed the efficiency of the integrated use of GIS and logistic regression, with emphasis on the relative simplicity of the model, speed of application and good ability to identify areas susceptible to landslides. The proposed model allowed the determination of the probability of occurrence of landslides with good predictive capacity, surpassing the usual model used by the Geological Survey of Brazil (CPRM).

Keywords: Natural disasters; Geoprocessing; Logistic regression; Landslides (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-03997-8

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