Mapping Geological at-Risk Areas in the City of São Paulo: Issues and Results from the Largest Risk Survey in Brazil

Pascarelli, Luciana; Lançone, Rafaela; Costa, Rodrigo Garrett da; Pires, Luiz F.; Macedo, Eduardo Soares de; Mirandola, Fabrício Araujo; Checchinato, Fabiana; Canil, Kátia

doi:10.1007/978-3-642-31319-6_47

Cited by 4 publications

(2 citation statements)

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“…Besides these analyses, other approach was adopted, based on the "risk levels" terminology used by CPRM, and considered by some recent studies (Listo and Vieira, 2012;Pascarelli et al, 2011). The classification considering "risk levels" provided by CPRM considers a hierarchical categorization of risk sectors, regarding the probability of landslide occurrence and its potential impacts.…”

Section: Validation Of Resultsmentioning

confidence: 99%

Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil

Camarinha

Canavesi

Alvalá

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. This study presents a methodology for susceptibility mapping of shallow landslides just from data and software from the public domain. The study was conducted in a mountainous region located on the southeastern Brazilian coast, in the state of São Paulo. The proposal is that the methodology can be replicated in a practical and reliable way in several other municipalities that do not have such mappings and that often suffer from landslide-related disasters. The susceptibility mapping was generated based on the following maps: geological, soils, slope, horizontal and vertical curvatures, and land use. The thematic classes of these maps were weighted according to technical and scientific criteria related to the triggering of landslides, and were crossed by the fuzzy gamma technique. The mapping was compared with the risk sector survey made by the Brazilian Geological Survey (CPRM), which is the official database used by municipalities and civil defense in risk management. The results showed positive correlations, so that the critical risk sectors had higher proportions for the more susceptible classes. To compare the approach with other studies using landslide-scar maps, correlated indices were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas.

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Section: Validation Of Resultsmentioning

confidence: 99%

Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil

Camarinha

Canavesi

Alvalá

2014

Nat. Hazards Earth Syst. Sci.

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“…(Online), São Paulo, v.18, n.4, (Out-Dez) p.719-735, 2017 In Brazil, several governmental institutions and scientifi c groups have suggested guidelines for assessing landslide susceptibility, using mainly geomorphological analyses (e.g. Macedo et al 2013;Pascarelli et al 2013) as well as deterministic approaches (e.g. Listo and Vieira 2012;Michel et al 2014;Nery and Vieira 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Analysis Applied to Landslide Susceptibility Mapping

Brito

Weber

Filho

2017

Rev. Bras. Geomorfol.

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Abstract:This paper presents the application of a multi-criteria analysis (MCA) tool for landslide susceptibility assessment in Porto Alegre municipality, southern Brazil. A knowledge driven approach was used, aiming to ensure an optimal use of the available information. The landslide conditioning factors considered were slope, lithology, fl ow accumulation and distance from lineaments. Standardization of these factors was done through fuzzy membership functions, and evaluation of their relative importance for landslide predisposition was supported by the analytic hierarchy process (AHP), based on local expert knowledge. Finally, factors were integrated in a GIS environment using the weighted linear combination (WLC) method. For validation, an inventory, including 107 landslide points recorded between 2007 and 2013 was used. Results indicated that 8.2% (39.40 km²) of the study area are highly and very highly susceptible to landslides. An overall accuracy of 95% was found, with an area under the receiver operating characteristic (ROC) curve of 0.960. Therefore, the resulting map can be regarded as useful for monitoring landslide-prone areas. Based on the fi ndings, it is concluded that the proposed method is eff ective for susceptibility assessment since it yielded meaningful results and does not require extensive input data. Resumo: Informações sobre o ArtigoEste estudo apresenta a aplicação de uma ferramenta de análise multi-critério para mapear a suscetibilidade a escorregamentos no município de Porto Alegre, sul do Brasil. Uma abordagem guiada pelo conhecimento de especialistas foi utilizada, com o intuito de otimizar a utilização das informações disponíveis. Os fatores condicionantes dos escorregamentos considerados foram declividade, litologia, acúmulo defl uxo e distância de lineamentos. A padronização desses fatores foi realizada por meio da aplicação de funções fuzzy e a importância relativa de cada um na predisposição do terreno a escorregamentos foi estabelecida com o apoio da técnica AHP (Analytic Hierarchy Process), com base no conhecimento de especialistas locais. Por fi m, a integração dos fatores em ambiente SIG se deu por meio do método denominado Combinação Linear Ponderada (WLC). Para validar os resultados, utilizou-se um mapa inventário contendo 107 cicatrizes de escorregamentos, registradas entre 2007 e 2013. Os resultados indicam que 8,2% (39,38 km²) da área de estudo possui uma suscetibilidade alta e muito alta a escorregamentos. A validação dos resultados obteve uma exatidão geral de 95%, com uma área abaixo da curva ROC (Receiver Operating Characteristic) de 0,960. Portanto, o mapa obtido pode ser considerado útil para monitorar as áreas propensas a esses processos. Com base nos resultados, conclui-se que o método proposto é efi caz para a avaliação da suscetibilidade, uma vez que os resultados obtidos são robustos e que não foi necessária uma quantidade extensa de dados de entrada.

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