Abstract
Shallow landslides are common in Brazil's urban areas. Geomorphology and land use are contributing factors, and rainfall is the triggering one. In these urban areas, anthropogenic activities that increase the level of landslide risk are common, such as cutting and filling or discharging wastewater onto the slopes. The Brazilian Government has developed a methodology to map the risk level in landslide-prone areas. The methodology is based on field observation and divides the risk into four main categories: low, moderate, high, and very high. Technicians in the field decide the sector's landslide risk level based on their professional and personal experiences, but without mathematical calculations or without using specific weights for the contributing factors. This study proposes a method for automatically computing the risk level by involving many experts for deriving each classifier weight, thereby reducing the subjectivity in selecting the final risk level. The weights were calculated using the Analytical Hierarchical Process based on 23 experts on landslides, and the standard deviation was used to define the risk level threshold. We validated the study using a prior risk mapping of São Paulo city. Finally, an application (app) that can be used on a tablet, computer, or smartphone was created to facilitate data collection during fieldwork and to automatically compute the risk level. Risk areas in Brazil are frequently changing as new residents move to the area or changes in the buildings or terrain are made. In addition, mapping the risk areas is expensive and time-demanding for municipalities. Therefore, an application that gathers the data easily and automatically computes the risk level can help municipalities rapidly update their risk sectors, allowing them to use updated risk mapping during the rainy season and be less dependent on rarely available financial resources to hire a risk mapping service.
This is a preview of subscription content, log in via an institution to check access.
Source: Goto, 2012). b Brick buildings in one of São Paulo's favelas. Both brick and mixed material are precarious buildings, but mixed material is still less resistant than brick buildings and can be easily destroyed. In the photograph, note the pipeline where the wastewater is released (Source: Goto, 2012)
Source: Goto, 2012). b Detailed photograph of the material mixed with soil (Source: Goto, 2012)
Similar content being viewed by others
Notes
Favela—the Portuguese name for slum and shanty town. By the IBGE, the Brazilian Institute of Geography and Statistics, favela or ‘aglomeramento subnormal’ means “the set with at least 51 dweller units with no property title and at least one of the following characteristics: irregular paths and size and shape of the allotment and/or lack of basic public service (official garbage collection, sewage network, water network, energy network and street lighting).
References
Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61(3):595–611. https://doi.org/10.1007/s12665-009-0373-1
Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir. Turkey Landslides 9:93. https://doi.org/10.1007/s10346-011-0283-7
Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81(4):432–445. https://doi.org/10.1016/j.enggeo.2005.08.004
Barredo J, Benavides A, Hervás J, van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J Appl Earth Obs Geoinf 2(1):9–23. https://doi.org/10.1016/S0303-2434(00)85022-9
BPMSG (Business Performance Management Singapore) (June 2018). New AHP Excel template with multiple inputs. https://bpmsg.com/new-ahp-excel-template-with-multiple-inputs/
Carvalho CS, de Macedo ES, Ogura AT (2007) Mapeamento de Riscos em Encostas e Margem de Rios. Ministério das Cidades & Instituto de Pesquisas Tecnológicas, Brasilia
CEPED UFSC. (2013) Atlas Brasileiro de Desastres Naturais: 1991 a 2012. CEPED UFSC, Florianópolis
De Blasio FV (2011) Friction, Cohesion, and Slope Stability. In: Introduction to the Physics of Landslides—lecture notes on the dynamics of mass wasting. Springer Science + Business Media B.V.
Dourado F, Arraes TC, Silva MF (2012) O Megadesastre da Região Serrana do Rio de Janeiro—as causas do evento, os mecanismos dos movimentos de massa e a distribuição espacial dos investimentos de reconstrução no pós-desastre. Anu Inst Geocienc 35(2):43–54.
Gorsevski PV, Jankowski P, Gessler PE (2006) An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35(1):121–146
Hasekioğulları GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179. https://doi.org/10.1007/s11069-012-0218-1
Highland LM, Bobrowsky P (2008) The landslide handbook—a guide to understanding landslides: Reston, Virginia, U.S. Geological Survey Circular 1325:129
IPT (2010) Mapeamento de Áreas de Risco—São Paulo
Long NT, De Smedt F (2012) Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district, Thua Thien Hue Province. Vietnam Environ Earth Sci 66(7):1739–1752
Malczewski J, Rinner C (2015) Multicriteria decision analysis in geographic information science. Springer-Verlag, Berlin
Maricato E (2008) Brasil, cidades: alternativas para a crise urbana. Vozes, Petropolis
Mezughi TH, Akhir JM, Rafek AG, Abdullah I (2012) Analytical Hierarchy Process method for mapping landslide susceptibility to an area along the E-W Highway (Gerik-Jeli), Malaysia. Asian J Earth Sci 5(1):13–24
Ministério das Cidades (June 2008). Ministério das Cidades. Retrieved from Ministério das Cidades. https://www.cidades.gov.br/
Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola Watershed, Darjiling: a remote sensing and GIS based analytical hierarchy process (AHP). J Ind Soc Remote Sens 40(3):483–496. https://doi.org/10.1007/s12524-011-0160-9
Nogueira FR (2002) Gerenciamento de riscos ambientais associados a escorregamentos: Contribuição Às Políticas Públicas Municipais (dissertation). Universidade Estadual Paulista, Rio Claro
Phukon P, Chetia D, Das P (2012) Landslide susceptibility assessment in the Guwahati City, Assam using Analytic hierarchy process (AHP) and geographic information system (GIS). Int J Comput Appl Eng Sci 2(1):1–6
Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed. Iran Natural Hazards 63(2):965–996
Rozos D, Bathrellos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: A case study from the Eastern Achaia County of Peloponnesus. GREECE Environ Earth Sci 63(1):49–63. https://doi.org/10.1007/s12665-010-0687-z
Saaty TL (1980) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83. https://doi.org/10.1504/IJSSCI.2008.017590
Sedan AH, Suv C (2013) Analytic hierarchy process. Power Point Present 1:1–22. https://doi.org/10.1002/9781118644898.ch2
Saaty TL (1990) How to make a decision: The analytic hierarchy process. Eur J Oper Res. https://doi.org/10.1016/0377-2217(90)90057-I
Tominaga LK, Santoro J, Amaral R (2009) Desastres naturais: conhecer para prevenir. Instituto Geologico, São Paulo
United Nations (2004). Living with Risk—a global review of disaster reduction, volume I, New York and Geneva.
Wu CH, Chen SC (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112(3–4):190–204. https://doi.org/10.1016/j.geomorph.2009.06.002
Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA 72(1):1–12. https://doi.org/10.1016/j.catena.2007.01.003
Yalcin A, Reis S, Cagdasoglu A, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85:274–287
Acknowledgement
We would like to thank CAPES and the Brazilian Government fellowship Science Without Borders for four years of fellowship and the opportunity to develop this study; prof. Dr. Eduardo Macedo for his contribution to understand the BGM; and all the experts who contributed to this research by taking out personal time to answer the pairwise comparison questionnaire.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1
1.1 AHP for shallow landslide risk areas
The AHP (Analytical Hierarchy Process) was a methodology developed by Thomas L. Saaty, and it computes specific weight values for classifiers based on their degree of influence. The AHP, in this study, will be used to compute the specific weight values of classifiers used to map shallow landslide in urban areas that are triggered by rainfall.
1.2 Instructions
The next questions will include natural, anthropogenic, and instability classifiers that contribute to the risk level of shallow landslides in urban areas.
Each question has two classifiers. You will select one classifier (A or B) that, in your opinion, contribute for a higher risk level for shallow landslide. Or, if you think both classifiers are equally important, you should select the option (1). If you selected one classifier (A or B), you will need to point out how much the classifier you selected is more important than the other classifier to increase the risk level (values between 2 and 9).
Explanations for the values 1 to 9 in the table below.
Values | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
equally importance | moderate importance | strong importance | very strong importance | extreme importance |
1.3 Questões
Part A: Natural factors, anthropogenic factor, and instability signs | ||||||||||||||||||||||||||||||||
Natural factors (A) or anthropogenic factors (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Natural factors (A) or instability signs (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Instability signs (A) or anthropogenic factors (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
PART B—Natural factors | ||||||||||||||||||||||||||||||||
Slope angle (A) or soil type(B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Slope angle (A) or natural land cover (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Slope angle (A) or geology of the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Soil type (A) or Natural land cover (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Soil type (A) or geology of the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Natural land cover (A) or geology of the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
B1—geology of the Terrain | ||||||||||||||||||||||||||||||||
Favorable to stability (A) or not observed (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Favorable to stability (A) or unfavorable to stability (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Not observed (A) or unfavorable to stability (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
B2—Slope angle in degree (ϴ) (ϴ < 10, 10 ≤ ϴ < 17, 17 ≤ ϴ < 30, 30 ≤ ϴ < 60, 60 ≤ ϴ < 90, ϴ = 90) | ||||||||||||||||||||||||||||||||
ϴ < 10 (A) or 10 ≤ ϴ < 17 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
ϴ < 10 (A) or 17 ≤ ϴ < 30 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
ϴ < 10 (A) or 30 ≤ ϴ < 60 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
ϴ < 10 (A) or 60 ≤ ϴ < 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
ϴ < 10(A) or ϴ = 90(B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
10 ≤ ϴ < 17 (A) or 17 ≤ ϴ < 30 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
10 ≤ ϴ < 17 (A) or 30 ≤ ϴ < 60 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
10 ≤ ϴ < 17 (A) or 60 ≤ ϴ < 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
10 ≤ ϴ < 17 (A) or ϴ = 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
17 ≤ ϴ < 30 (A) or 30 ≤ ϴ < 60 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
17 ≤ ϴ < 30 (A) or 60 ≤ ϴ < 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
17 ≤ ϴ < 30 (A) or ϴ = 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
30 ≤ ϴ < 60 (A) or ϴ = 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
30 ≤ ϴ < 60 (A) or 60 ≤ ϴ < 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
60 ≤ ϴ < 90 (A) or ϴ = 90 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
PART C: Anthropogenic factors: (Type of building: look into C1, building position: look into C2, building density: look into C3) | ||||||||||||||||||||||||||||||||
Type of building (A) or water in the terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Type of building (A) Or garbage in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Type of building (A) or building position (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Type of building (A) or building density (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Water in the Terrain A) or building position (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Water in the Terrain (A) or garbage in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Water in the Terrain (A) or building density (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Garbage in the Terrain (A) or building position (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Garbage in the Terrain (A) or building density (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Building position (A) or building density (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
C1—Type of building Which type of building contributes for a higher shallow landslide risk level? | ||||||||||||||||||||||||||||||||
Wood (A) or brick (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Wood (A) or mixed material (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Mixed material (A) or brick (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
C2—Building position | ||||||||||||||||||||||||||||||||
Near the slope base (A) or near the slope top (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Near the slope base (A) or middle of the slope (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Near the slope base (A) or far away from the slope top (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Near the slope base (A) or far away from the slope base (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Middle of the slope (A) or near the slope top (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Middle of the slope (A) or far away from the slope base (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Middle of the slope (A) or far away from the slope top (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Near the slope top (A) or far away from the slope base (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Near the slope top (A) or far away from the slope top (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Far Away from the slope base (A) or far away from the slope top (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
C3—Density | ||||||||||||||||||||||||||||||||
D1: dense occupied area with basic infrastructure | ||||||||||||||||||||||||||||||||
D2: in the process of being occupied, neighborhood D1. Density between 30 to 90%. Acceptable basic infrastructure | ||||||||||||||||||||||||||||||||
D3: expansion area, outskirt or far away from urbanized areas. Low density (bellow 30%). No basic infrastructure | ||||||||||||||||||||||||||||||||
D4: based on the density and basic infrastructure available | ||||||||||||||||||||||||||||||||
D1 (A) or D2 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
D1 (A) or D3 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
D1 (A) or D4B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
D2 (A) or D3(B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
D2 (A) or D4 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
D3 (A) or D4 (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
C2—Water in the terrain | ||||||||||||||||||||||||||||||||
Wastewater (A) or rainfall water Accumulated in the surface (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Wastewater (A) or pipe leak (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Wastewater (A) or septic tank (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Wastewater (A) or drainage type (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Rainfall Water Accumulated in the surface (A) or pipe leak (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Rainfall water accumulated in the surface (A) or septic tank (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Rainfall water accumulated in the surface (A) or drainage type (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Pipe leak (A) or septic tank (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Pipe leak (A) or drainage type (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
C3—Drainage type | ||||||||||||||||||||||||||||||||
None (A) or precarious (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
None (A) or okay (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Precarious (A) or okay (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
C4—Land cover | ||||||||||||||||||||||||||||||||
Tree (A) or shrub (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree (A) or deforestation/nude soil (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree (A) or grass (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree (A) or banana tree (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree (A) or urban coverage (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Shrub (A) or deforestation/nude soil (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Shrub (A) or grass (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Shrub (A) or banana tree (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Shrub (A) or urban coverage (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Deforestation/nude soil (A) or grass (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Deforestation/nude soil (A) or banana tree (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Deforestation/nude soil (A) or urban coverage (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Grass (A) or banana tree (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Grass (A) or urban coverage (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Banana tree (A) or urban coverage (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
D—Instability Signs | ||||||||||||||||||||||||||||||||
Leaning wall (A) or crack in the building (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Leaning wall ( A) or tree, pole, wall tilted (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Leaning wall ( (A) or downward floor (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Leaning wall ( (A) or landslide scar (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Leaning wall ( (A) or crack in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Crack in the building (A) or tree, pole, wall tilted (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Crack in the building (A) or downward floor (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Crack in the building (A) or landslide scar (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Crack in the building (A) or Crack in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree, pole, wall tilted (A) or downward floor (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree, pole, wall tilted (A) or landslide Scar (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Tree, pole, wall tilted (A) or crack in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Downward floor (A) or landslide scar (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Downward floor (A) or crack in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Landslide scar (A) or crack in the Terrain (B) | A | B | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||||||||||||||
Appendix 2
Respondent | Institution | Field | Time working with landslide assessment (years) | Age (years) | Gender |
|---|---|---|---|---|---|
Respondent 1 | JICA1 | Geology engineering | 10 | 30 | Female |
Respondent 2 | IPT 2 | Geology | 10 | 45 | Male |
Respondent 3 | Civil Protection of City of Jundiai | Geology | 3.5 | 27 | Male |
Respondent 4 | Ministry of Habitation of city of São Bernardo do Campo | Geology | 7 | 43 | Female |
Respondent 5 | COMPDEC3 , Castelo—Espirito Santo State | Geology | 8 | 35 | Female |
Respondent 6 | DRM4 – Rio de Janeiro State | Geology | 10 | 32 | Female |
Respondent 7 | Cemaden | Geography | 9 | 34 | Male |
Respondent 8 | Regea 5 | Geology | 40 | Male | |
Respondent 9 | UFABC 6 | Geography | 48 | Female | |
Respondent 10 | Santana de Parnaiba Municipality | Geology | 15 | 54 | Male |
Respondent 11 | Cemaden | Geology | 25 | 50 | Male |
Respondent 12 | Cemaden | Geography | 11 | Male | |
Respondent 13 | IPT | Civil technician | 25 | 58 | Male |
Respondent 14 | IPT | Civil engineering | 38 | 63 | Male |
Respondent 15 | IPT | Geology | 3 | 32 | Female |
Respondent 16 | IPT | Civil engineering | 3 | 30 | Female |
Respondent 17 | IPT | Geology | 13 | 46 | Female |
Respondent 18 | IPT | Chemistry | 20 | 54 | Male |
Respondent 19 | IPT | Geology | 30 | 61 | Male |
Respondent 20 | IPT | Civil technician | 37 | 61 | Male |
Respondent 21 | São Paulo Municipality | Geology | 25 | 59 | Male |
Respondent 22 | São Paulo Civil Protection | Geoscience | 4 | 26 | Female |
Respondent 23 | Santos Civil Protection | Geology | 4 months | 26 | Male |
Appendix 3
We used answers from expert 10 and Section A of the survey to explain how AHP computes each criterion weight and CR of each expert.
Based on the choices made by expert 10, we built the matrix for each level of the hierarchy structure. Table
6 illustrates expert '10A's choices for the pairwise comparisons on Section A of the survey. Section A compares three criteria: (i) natural aspects; (ii) anthropogenic aspects; and (iii) instability signs. For each pairwise comparison, the expert chooses which criterion is more important (a or b) and how many times it is more important based on ranking table (Table
7). In the case of a and b having the same weight, the choice is 1.
The start table is represented by Table
8 and illustrates the possible combinations based on the pairwise comparison. It represents numerical values of a matrix A with n x n, where n is the number of criteria, in this case n = 3. Each entry of the matrix A is represented by ajk and it represents the importance of criterion j relative to the criterion k. If ajk > 1, criterion j is more important than criterion k, if ajk < 1 criterion k is more important than criterion j, and if ajk = 1, both criteria have the same importance.
Table
9 illustrates Table 8 filled with numerical values based on expert '10′s choices. The green cells are filled with expert answers, and the yellow cells are filled by the opposite value revealed by the 'expert's choice. For instance, the pairwise comparison of N and I has a numerical value equal to 1/3 (row N and column I) and the cell that represents the opposite pairwise comparison (row I and column N) is filled with the inverse numerical value (3).
After filling the table with numerical values that represent the pairwise comparisons, we computed the specific weight of the criteria by summing the values on N, A, and I in each column (Table
10) and then normalizing the values. Equation 4 represents how each value (\({\overline{a }}_{jk}\)) of the normalized matrix Anorm is computed, and Eq. 5 illustrates how the weight for each criterion is computed. Columns in blue from Table
11 represents matrix Anorm.
where n is the number of criteria, \({\overline{a }}_{jk}\) is the normalized value of ajk and Wj is the specific weight of each criterion.
Columns N, A, and I in Table 11 are the normalized values and the column Priority (W) is the specific weight of each criterion computed with Eq. 5.
After computing the specific weight for each criterion of the hierarchy level based on expert 10, we are interested in computing the CR. The AHP verifies consistency of the pairwise comparison of each hierarchy level by computing the CR. Values with CR larger or equal to 0.1 are not used since they are considered random.
To compute CR, we first compute \({\lambda }_{max}\) and CI (Consistency Index) with Eqs. 6 and 7, respectively.
In our example, \({A}_{sum}=(5, 3, 2.33)\) and \(\mathrm{W}=(0.23, 0.32, 0.45)\). Therefore, \({\lambda }_{max}=\left(5*0.23+3*0.32+2.33*0.45\right)=3.15\). Computing CI with n = 3, the numerical value is 0.074.
Using Table
12 and Eq. 8, we compute the CR of expert 10.
In our example, n = 3, so RI is equal to 0.58. Plugging these numbers into Eq. 8, we find CR equal to 0.128 (around 13%). Since CR is higher than 0.1, pairwise comparison for this hierarchical level is considered random and should not be used.
Appendix 4
4.1 Classifier, category, and specific weight value
Classifier | Category | Specific weight |
|---|---|---|
α < 10 | N | 0.000 |
10 ≤ α < 17 | N | 0.004 |
17 ≤ α < 30 | N | 0.013 |
30 ≤ α < 60 | N | 0.027 |
60 ≤ α < 90 | N | 0.057 |
α = 90 | N | 0.058 |
Soil favorable to instability (yes) | N | 0.047 |
Soil favorable to instability (not observed) | N | 0.024 |
Soil favorable to instability (no) | N | 0.000 |
Natural coverage favorable to instability (yes) | N | 0.019 |
Natural coverage favorable to instability (not observed) | N | 0.010 |
Natural coverage favorable to instability (no) | N | 0.000 |
Geology favorable to instability (yes) | N | 0.047 |
Geology favorable to instability (not observed) | N | 0.024 |
Geology favorable to instability (no) | N | 0.000 |
Wood | A | 0.030 |
Brick | A | 0.000 |
Mix material | A | 0.036 |
Near slope base | A | 0.012 |
Near slope top | A | 0.009 |
Far away from slope base | A | 0.002 |
Far away from slope top | A | 0.002 |
Middle of the slope | A | 0.012 |
Consolidated sector | A | 0.000 |
Partially consolidated sector | A | 0.023 |
Developing sector | A | 0.029 |
Mix sector | A | 0.019 |
Waste water | A | 0.010 |
Concentration of rainfall water (surface) | A | 0.010 |
Leak | A | 0.019 |
Septic tank | A | 0.008 |
Satisfy drainage system | A | 0.000 |
Drainage system precarious | A | 0.005 |
No drainage system | A | 0.006 |
Tree | A | 0.004 |
Shrubs/bushes | A | 0.003 |
Deforestation/exposed soil | A | 0.011 |
Grass/ground vegetation | A | 0.004 |
Banana tree | A | 0.012 |
Urban coverage | A | 0.003 |
Garbage, land fill, or deposit | A | 0.038 |
Leaning wall | I | 0.080 |
Crack in the house | I | 0.075 |
Crack In the terrain | I | 0.111 |
Tilted Trees, poles | I | 0.080 |
Downward sloping floor | I | 0.142 |
Landslide scars | I | 0.107 |
Rights and permissions
About this article
Cite this article
Goto, E.A., Clarke, K. Using expert knowledge to map the level of risk of shallow landslides in Brazil. Nat Hazards 108, 1701–1729 (2021). https://doi.org/10.1007/s11069-021-04752-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-021-04752-3