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A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil

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Abstract

Statistically based landslide susceptibility mapping has become an important research area in the last decades, and several bivariate and multivariate statistical approaches to landslide susceptibility assessments have been applied and compared in all regions of the world. The aim of this study was to compare different statistical approaches and to analyse the degree of spatial agreement between the landslide susceptibility maps produced. To this end, we selected seven statistical methods for comparison, namely, landslide density, likelihood ratio, information value, Bayesian model, weights of evidence, logistic regression and discriminant analysis, and then applied these to an inventory comprising 940 translational landslides, in the southeast region of Minas Gerais state in Brazil, at the western edge of the Quadrilátero Ferrífero (642.13 km2). In some statistical approaches, modifications were made to the input dependent variables. The landslides registered in the inventory map have been used in punctual and polygonal form. Six factors were considered as input landslide predisposing factors: slope angle, geomorphological units, slope curvature, lithological units, slope aspect and inverse wetness index. The combination order of the landslide predisposing factors was established based on a sensitivity analysis, which gave rise to five different cartographic combinations. In total, 58 statistical models of landslide susceptibility were produced, and the results were validated using success and prediction rate curves. The spatial agreement evaluation between the model results was carried out with kappa statistics. There were 214 comparisons of spatial agreement involving classified models at three relative degrees of susceptibility (high, medium and low landslide susceptibility classes). The results showed that all of the models so produced had satisfactory validation rates. The best landslide susceptibility models obtained areas under the curve of > 0.80 in the success and prediction rate curves, with emphasis on the weights of evidence, the information value and the likelihood ratio statistical methods. These statistical approaches were performed with the landslides mapped in the form of points. The landslide susceptibility classes of these models visually demonstrated a slightly more irregular spatial distribution when compared to the models performed with landslide polygons. The likelihood ratio model performed with landslide points presented one of the smallest areas for the high susceptibility class and the largest area for the low susceptibility class. The analysis of the spatial agreement showed that the models produced with a polygonal dependent variable tend to be more concordant, regardless of the statistical technique used. Moreover, we verified that spatial agreement tends to increase with increasing accuracy of the models. Despite the discrepancies found, most of the models compared showed a substantial or almost perfect degree of agreement.

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References

  • Ahmed MF, Rogers JD (2014) First-approximation landslide inventory maps for northern Pakistan, using ASTER DEM data and geomorphic indicators. Environ Eng Geosci 20:67–83

    Article  Google Scholar 

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44

    Article  Google Scholar 

  • Alkmim FF, Marshak S (1998) Trans-Amazonian Orogeny in the Southern São Francisco Craton Region, Minas Gerais, Brazil: evidence for Paleoproterozoic collision and collapse in the Quadrilátero Ferrífero. Precambrian Res 90:29–58

    Article  Google Scholar 

  • Atkinson P, Jiskoot H, Massari R, Murray T (1998) Generalized linear modelling in geomorphology. Earth Surf Process Landf 23:1185–1195

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Process Landf 26:1251–1263

    Article  Google Scholar 

  • Barella CF, Sobreira FG (2015) Análise da susceptibilidade a escorregamentos usando a abordagem estatística do fator de certeza no município de Moeda, Minas Gerais. Revista Brasileira de Geologia de Engenharia e Ambiental 5:55–66

    Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Blahut J, van Westen CJ, Sterlacchini S (2010) Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology 119:36–51

    Article  Google Scholar 

  • Blattberg RC, Kim B, Neslin SA (2008) Database marketing: analysing and managing customers. Springer SBM, New York

    Book  Google Scholar 

  • Brazil (2012) Lei Nº 12.608 - Institui a Política Nacional de Proteção e Defesa Civil – PNPDEC. Diário Oficial da União, Brasília

  • Carrara A (1993) Uncertainty in evaluating landslide hazard and risk. In: Nemec J, Nigg JM, Siccardi F (eds) Prediction and perception of natural hazards. Advances in natural and technological hazards research, vol 2. Springer, Dordrecht, pp 101–109

    Chapter  Google Scholar 

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16:427–445

    Article  Google Scholar 

  • Cervi F, Berti M, Borgatti L, Ronchetti F, Manenti F, Corsini A (2010) Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy). Landslides 7:433–444

    Article  Google Scholar 

  • Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65:1389–1399

    Google Scholar 

  • Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46

    Article  Google Scholar 

  • Corominas J, Mavrouli O (2011) Recommended procedures for validating landslide hazard and risk models and maps. Safe land: living with landslide risk in Europe: assessment, effects of global change and risk management strategies. Norwegian Geotechnical Institute, Oslo

  • Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Herva’s J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263

    Google Scholar 

  • Dikau R (1990) Derivatives from detailed geoscientific maps using computer methods. Z Geomorphol 80:45–55

    Google Scholar 

  • Dorr JVN (1969) Physiographic, stratigraphic and structural development of Quadrilátero Ferrífero, Minas Gerais, Brazil. Professional Paper 641-A. USGS/DNPM, Washington DC

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102:85–98

    Article  Google Scholar 

  • Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111:62–72

    Article  Google Scholar 

  • Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modelling. Comput Geosci 81:1–11

    Article  Google Scholar 

  • Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386

    Article  Google Scholar 

  • Guha-Sapir D, Below R, Hoyois P (2017) EM-DAT: The CRED/OFDA International Disaster Database. Université Catholique de Louvain, Brussels. www.emdat.be

    Google Scholar 

  • Guha-Sapir D, Hargitt D, Hoyois P (2004) Thirty years of natural disasters 1974–2003: the numbers. Presses Universitaires de Louvain, Belgium

    Google Scholar 

  • Guri PK, Champati ray PK, Patel RC (2015) Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling. Environ Monit Assess 187(324):1–25

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study. Central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K (2012) Landslide inventory maps: New tools for an old problem. Earth–Sci Rev 112:42–66

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184

    Article  Google Scholar 

  • Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazards Earth Syst Sci 14:259–278

    Article  Google Scholar 

  • Heineck CA, Leite CAS, Silva MA, Vieira VS (2003). Mapa Geológico do Estado de Minas Gerais. CPRM, CODEMIG e Governo do Estado de Minas Gerais. http://rigeo.cprm.gov.br/xmlui/handle/doc/5016

  • Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 13:379–397

    Article  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  Google Scholar 

  • Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34:223–232

    Article  Google Scholar 

  • Lee S, Ryu J, Kim I (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin. Korea. Landslides 4:327–338

    Article  Google Scholar 

  • Lobato LM, Baltazar OF, Reis LB, Achtschin AB, Baars FJ, Timbó MA, Berni GV, Mendonça BRV, Ferreira DV (2005) Projeto Geologia do Quadrilátero Ferrífero—Integração e Correção Cartográfica em SIG com Nota Explicativa. CODEMIG, Belo Horizonte

  • Marques RTF (2013) Estudos de Movimentos de Vertente no Conselho da Povoação (Ilha de São Miguel, Açores): Inventariação, Caracterização e Análise da Susceptibilidade. PhD thesis. Department of Geosciences, University of the Azores, Portugal

  • Medina AIM, Dantas ME, Saadi A (2005) Projeto APA Sul RMBH—Estudos do Meio Físico–Geomorfologia. SEMAD/CPRM, Belo Horizonte

    Google Scholar 

  • Montz BE, Tobin GA, Hagelman RR (2017) Natural hazards: explanation and integration. The Guilford Press, New York

    Google Scholar 

  • Neuhäuser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model. Landslides 9:511–528

    Article  Google Scholar 

  • Nobre AD, Cuartas LA, Hodnett M, Rennó CD, Rodrigues G, Silveira A, Waterloo M, Saleska S (2011) Height above the nearest drainage—a hydrologically relevant new terrain model. J Hydrol 404:13–29

    Article  Google Scholar 

  • Oliveira SC, Zêzere JL, Garcia RAC (2015a) Structure and characteristics of landslide input data and consequences on landslide susceptibility assessment and prediction capability. In: Lollino G, Giordan D, Crosta GB, Corominas J, Azzam R, Wasowski J, Sciarra N (eds) Eng Geol for Society and Territory, vol 2. Springer, Cham, pp 189–192

  • Oliveira SC, Zêzere JL, Catalão J, Nico G (2015b) The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas. Landslides 12:703–719

    Article  Google Scholar 

  • Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. SpringerPlus 2:523 1–11

    Article  Google Scholar 

  • Pereira S, Garcia RAC, Zêzere JL, Oliveira SC, Silva M (2016) Landslide quantitative risk analysis of buildings at the municipal scale based on a rainfall triggering scenario. Geomat Nat Haz Risk 1–25

  • Pereira S, Zêzere JL, Bateira C (2012) Technical note: assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models. Nat Hazards Earth System Sci 12:979–988

    Article  Google Scholar 

  • Pereira SS (2009) Perigosidade a Movimentos de Vertente na Região Norte de Portugal. PhD thesis. Department of Geography, University of Porto, Portugal

  • Petschko H, Bell R, Leopold P, Heiss G, Glade T (2013) Landslide inventories for reliable susceptibility maps in Lower Austria. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice, vol 1: Landslide inventory and susceptibility and hazard zoning. Springer, Berlin Heidelberg, pp 281–286

  • Petschko H, Brenning A, Bell R, Goetz J, Glade T (2014) Assessing the quality of landslide susceptibility maps – case study Lower Austria. Nat Hazards Earth Syst Sci 14:95–118

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I (2018a) Bagging based Support Vector Machines for spatial prediction of landslides. Environ Earth Sci 77(146):1–17

    Google Scholar 

  • Pham BT, Prakash I (2017) A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment. Bull Eng Geol Environ 1–15

  • Pham BT, Prakash I, Bui DT (2018b) Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees. Geomorphology 303:256–270

    Article  Google Scholar 

  • Pham BT, Shirzadi A, Bui DT, Prakash I, Dholakia MB (2017) A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India. Int J Sed Res 33:157–170

  • Piedade A, Zêzere JL, Garcia R, Oliveira S (2011) Modelos de susceptibilidade a deslizamentos superficiais translacionais na Região a Norte de Lisboa. Finisterra 46:9–26

    Google Scholar 

  • Piedade A, Zêzere JL, Garcia RAC, Oliveira SC (2010) Avaliação e validação de modelos de susceptibilidade a deslizamentos em áreas homogéneas na região a Norte de Lisboa. In: 16th Cong APDR—Regiões de Charneira, Canais de Fronteira e Nós, Universidade da Madeira, Funchal, pp 1305–1319

  • Ponçano WL, Carneiro CDR, Almeida MA, Pires Neto AG, Almeida FFM (1979) O conceito de sistemas de relevo aplicado ao mapeamento geomorfológico do estado de São Paulo. In: Second Simpósio Regional de Geologia. SBG, Rio Claro, pp 253–262

  • Poonam, Rana N, Champati ray PK, Bisht P, Bagri DS, Wasson RJ, Sundriyal Y (2017) Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini valley, (central Himalaya), for disaster governance using the Weights of Evidence method. Geomorphology 284:41–52

    Article  Google Scholar 

  • Qin C, Zhu A, Pei T, Li B, Scholten T, Behrens T, Zhou C (2011) An approach to computing topographic wetness index based on maximum downslope gradient. Precis Agric 12:32–43

    Article  Google Scholar 

  • Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth-Sci Rev 180:60–91

    Article  Google Scholar 

  • Reis E, Zêzere JL, Vieira GT, Rodrigues ML (2003) Integração de dados espaciais em SIG para avaliação da susceptibilidade de ocorrência de deslizamentos. Finisterrra 38:3–34

    Google Scholar 

  • Rodriguez J, Vos F, Below R, Guha-Sapir D (2009) Annual disaster statistical review 2008: the numbers and trends. Centre for Research on the Epidemiology of Disasters (CRED), Brussels

  • Sawatzky DL, Raines GL, Bonham-Carter GF, Looney CG (2009) Spatial Data Modeller (SDM): ArcMAP 9.3 geoprocessing tools for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural networks. http://www.ige.unicamp.br/sdm/ArcSDM93

  • Schuster RL, Highland LM (2001) Socioeconomic and environmental impacts of landslide in the Western Hemisphere. U.S. Geological Survey, Open-File Report 01-0276. U.S. Geological Survey, Reston, VA. http://pubs.usgs.gov/of/2001/ofr-01-0276

  • Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 115:55–70

    Article  Google Scholar 

  • Soeters R, van Westen CJ (1996) Slope instability recognition, analysis, and zonation. In: Turner AK, Shuster RL (eds) Landslides: investigation and mitigation. Transportation Research Board. Natl Res Counc Spec Rep 247:129–177

  • Sørensen R, Zinko U, Seibert J (2006) On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol Earth Syst Sci 10:101–112

    Article  Google Scholar 

  • Sterlacchini S, Ballabio C, Blahut J, Masetti M, Sorichetta A (2011) Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology 125:51–61

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679

    Article  Google Scholar 

  • Tarboton DG (2015) Terrain analysis using digital elevation models (TauDEM). Hydrology Research Group, Utah State University. http://hydrology.usu.edu/taudem/taudem5/index.html

  • Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9:507–521

    Article  Google Scholar 

  • Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410

    Article  Google Scholar 

  • van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation. PhD thesis. International Institute for Aerospace Survey and Earth Sciences, Technical University Delft, Delft

  • van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131

    Article  Google Scholar 

  • Varnes DJ, IAEG Commission on Landslides and other Mass-Movements (1984) Landslide hazard zonation: a review of principles and practice. UNESCO, Paris

    Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides—analysis and control. Transport Res Board Spec Rep 176:11–33

  • Vos F, Rodriguez J, Below R, Guha-Sapir D (2010) Annual disaster statistical review 2009: the numbers and trends. Centre for Research on the Epidemiology of Disasters (CRED), Brussels

  • Wysocki DA, Schoeneberger PJ, Hirmas DR, LaGarry HE (2011) Geomorphology of soil landscapes. In: Huang PM, Li Y, Sumner ME (eds) Handbook of soil sciences properties and processes. CRC Press, Boca Raton, pp 1–26

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2015) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73:3745–3761

    Article  Google Scholar 

  • Zêzere JL (2002) Landslide susceptibility assessment considering landslide typology. A case study in the area north of Lisbon (Portugal). Nat Hazards Earth Syst Sci 2:73–82

    Article  Google Scholar 

  • Zêzere JL (2006) Predição probabilística de movimentos de vertente na escala regional. Actes de les Jornades sobreTerrasses i Prevenció de Riscos Naturals. Department de Medi Ambient, Mallorca, pp 17–30

  • Zêzere JL, Garcia RAC, Oliveira SC, Reis E (2005) Análise sensitiva na avaliação da susceptibilidade a deslizamentos na região a norte de Lisboa. X Colóquio Ibérico de Geografia. APG, Évora. www.apgeo.pt/files/docs/CD_X_Coloquio_Iberico_ Geografia/pdfs/075.pdf

  • Zêzere JL, Henriques CS, Garcia RAC, Oliveira SC, Piedade A, Neves M (2009) Effects of landslide inventories uncertainty on landslide susceptibility modelling. In: Malet JP, Remaître A, Boggard T (eds) Proc Landslide Processes. From Geomorphologic Mapping to Dynamic Modeling Conference. Utrecht University and University of Strasbourg, Utrecht, the Netherlands/Strasbourg, France, pp 81–86

  • Zêzere JL, Pereira S, Melo R, Oliveira SC, Garcia RAC (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the Ministry of the Cities in Brazil and RISKam, the Research Group of the Centre of Geographical Studies (CEG), the Institute of Geography and Spatial Planning, the University of Lisbon (IGOT-U Lisboa). CAPES financially supported this research via the Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Education Ministry of Brazil.

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  1. Environmental Engineering Department, Federal University of Ouro Preto, Morro do Cruzeiro, Ouro Preto, Minas Gerais, 35400-000, Brazil

    Cesar Falcão Barella & Frederico Garcia Sobreira

  2. Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Edifício IGOT, Rua Branca Edmée Marques, 1600-276, Lisbon, Portugal

    José Luís Zêzere

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Barella, C.F., Sobreira, F.G. & Zêzere, J.L. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil. Bull Eng Geol Environ 78, 3205–3221 (2019). https://doi.org/10.1007/s10064-018-1341-3

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