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Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data

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Abstract

The city of Ouro Preto, which is located in the state of Minas Gerais, Brazil, has a long history of mass movements influenced by the regional geology, geomorphology, and anthropic activities, which have resulted in harmful consequences to the population. However, most of the studies conducted in the region are qualitative and are directly dependent on the experience specialists. The aim of this study was to analyse the landslide susceptibility in the urban region of Ouro Preto quantitatively by using discriminant analysis. The landslide inventory was obtained by using unmanned aerial vehicle images and fieldwork. ArcGIS 10.6 and R 3.5.1 software were used, and the following landslide predisposing factors were considered: slope angle, slope aspect, profile curvature, and topographic wetness index (TWI). As geological and geotechnical data are still scarce in the interior of Brazil, we only used data derived from topography to determine the effectiveness of these factors for analysing landslide susceptibility. The slope angle proved to be the factor that most differentiated unstable from stable terrain, followed by TWI. The other parameters were not as effective in differentiating the stability conditions. The model efficiency was 88.6%, the specificity was 93.3%, and the sensitivity was 85.0%. Also, the prediction and success curve were used to evaluate the accuracy of the proposed landslides model, by using the area under the curve (AUC) criteria. It was shown that the AUC values 0.851 for testing and 0.838 for training indicate that the developed model provides an excellent prediction. The main contribution of this work is the demonstration of the effectiveness of using easily accessible data (derived from topography) for analysing landslide susceptibility with a multivariate statistical method. This method can contribute valuable information to urban planning efforts in cities without the need for robust data.

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References

  • 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 EngGeolEnviron 58:21–44

    Google Scholar 

  • Augusto Filho O (2001) Carta de risco de escorregamentos quantificada em ambiente de SIG como subsidio para planos de seguros em áreas urbanas: um ensaio em Caraguatatuba (SP). Universidade Estadual Paulista, Thesis

    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 Geomorphol 65(1–2):15–31

    Article  Google Scholar 

  • Barella CF (2016) Abordagens estatísticas aplicadas ao mapeamento de susceptibilidade a movimentos de massa: análise de diferentes técnicas no contexto do Quadrilátero Ferrífero. Universidade Federal de Ouro Preto, Thesis

    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 

  • Barella CF, Sobreira FG, Zêzere JL (2019) A comparative analysis of statistical land slide susceptibility mapping in the south east region of Minas Gerais state. Brazil Bull Eng Geol Environ 78(5):3205–3221

    Article  Google Scholar 

  • Beguería S, Lorente A (2002) Landslide hazard mapping by multivariate statistics: comparison of methods and case study in the Spanish Pyrenees. Instituto Pirenaico de Ecología, Saragossa, Technical report, p 19

    Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. HydrolSciJor 24(1):43–69

    Google Scholar 

  • Bitar OY (2014) Cartas de suscetibilidade a movimentos gravitacionais de massa e inundações – 1: 25.000: nota técnica explicativa. IPT; CPRM

  • Bonuccelli T, Zuquette LV (1999) Movimentos gravitacionais de massa e erosões na cidade histórica de Ouro Preto, Brasil. Revista Portuguesa de Geotecnia 85:59–80

    Google Scholar 

  • Bui DT, Moayedi H, Kalantar B, Osouli A, Gör M, Pradhan B, Rashid ASA (2019) Harris hawks optimization: a novel swarm intelligence technique for spatial assessment of landslide susceptibility. Sensors 19(16):3590

    Article  Google Scholar 

  • Carrara A (1983) Multivariate models for landslide hazard evaluation. J Int Assoc Math Geol 15(3):403–426

    Article  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 Proc Land 16(5):427–445

    Article  Google Scholar 

  • Castellanos Abella EA, Van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslides 4(4):311–325

    Article  Google Scholar 

  • CEPED-UFSC (2012) Atlas brasileiro de desastres naturais 1991 a 2010.Volume Brasil, Florianópolis

  • Chen C-Y, Chang J-M (2016) Landslide dam formation susceptibility analysis based on geomorphic features. Landslides 13(5):1019–1033

    Article  Google Scholar 

  • Chen Z, Wang J (2007) Landslide hazard mapping using logistic regression model in Mackenzie Valley. Canada Nat Hazards 42(1):75–89

    Article  Google Scholar 

  • Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogram Eng Remote Sens 65(12):1389–1399

    Google Scholar 

  • Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472

    Article  Google Scholar 

  • Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). CATENA 113:236–250

    Article  Google Scholar 

  • Corominas J, VanWesten 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, Hervás J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Env 73(2):209–263

    Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island. Hong Kong Geomorphology 42(3–4):213–228

    Google Scholar 

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

  • Eiras, CGS (2017) Mapeamento da suscetibilidade a eventos perigosos de natureza geológica e hidrológica em São Carlos-SP. Dissertation, Universidade de São Paulo

  • Fávero LP, Belfiore P, Silva LP, Chan BL (2009) Análise Discriminante. Análise de dados: modelagem multivariada para tomada de decisões,1st edn. Campus, Rio de Janeiro, pp 401–436

    Google Scholar 

  • Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annalsofeugenics 7(2):179–188

    Google Scholar 

  • Gaprindashvili G, Van Westen CJ (2016) Generation of a national landslide hazard and risk map for the country of Georgia. Nat Hazards 80(1):69–101

    Article  Google Scholar 

  • Garcia R, Zêzere JL, Oliveira S, Reis E (2007) A importância do processo de classificação de dados na cartografia: um exemplo na cartografia de susceptibilidade a movimentos de vertente. Publicações da Associação Portuguesa de Geomorfólogos 5:265–279

    Google Scholar 

  • Garcia RAC (2002) Avaliação do risco de movimentos de vertente na Depressão da Abadia (Torres Vedras). Dissertation, Universidade de Lisboa

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

    Article  Google Scholar 

  • Günther A, Van Den Eeckhaut M, Malet JP, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85

    Article  Google Scholar 

  • Guo C, Montgomery DR, Zhang Y, Wang K, Yang Z (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299

    Article  Google Scholar 

  • Hadmoko DS, Lavigne F, Samodra G (2017) Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia. Nat Hazards 87:437–468

    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 SystSci 14(2):259–278

    Article  Google Scholar 

  • Holsbach N, Fogliatto FS, Anzanello MJ (2014) Método de mineração de dados para identificação de câncer de mama baseado na seleção de variáveis. Ciência&SaúdeColetiva 19:1295–1304

    Google Scholar 

  • Hong H, Shahabi H, Shirzadi A, Chen W, Chapi K, Ahmad BB, Roodposhti MS, Hesar AY, Tian Y, Bui DT (2019) Landslide susceptibility assessment at the Wuning area, China: A comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Nat Hazards 96(1):173–212

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed. Nepal Nat Hazards 63(2):479–498

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin. Korea Environ Geology 40(9):1095–1113

    Article  Google Scholar 

  • Marôco J (2014) Análise Estatística com o SPSS Statistics, 6th edn. Report Number, Lisboa

    Google Scholar 

  • Meng Q, Miao F, Zhen J, Wang X, Wang A, Peng Y, Fan Q (2016) GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bull Eng Geol Environ 75:923–944

    Article  Google Scholar 

  • Moayedi H, Osouli A, Tien Bui D, Foong LK (2019) Spatial landslide susceptibility assessment based on novel neural-metaheuristic geographic information system based ensembles. Sensors 19(21):4698

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30

    Article  Google Scholar 

  • Myronidis D, Papageorgiou C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81(1):245–263

    Article  Google Scholar 

  • Nalon MA (2000) Mapeamento de risco de escorregamento na região de Cubatão. Thesis, Universidade de São Paulo, SP

    Google Scholar 

  • Nguyen H, Mehrabi M, Kalantar B, Moayedi H, Abdullahi MAM (2019) Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping. Geomat Nat Haz Risk 10(1):1667–1693

    Article  Google Scholar 

  • Nola ITS (2015) Avaliação de dados geológico-geotécnicos prévios para elaboração de carta de eventos perigosos de movimentos de massa gravitacionais por meio de redes neurais artificiais e probabilidade. Universidade de São Paulo, Thesis

    Book  Google Scholar 

  • Pham BT, Bui DT, Dholakia M, Prakash I, Pham HV (2016a) A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotech Geol Eng 34:1807–1824

    Article  Google Scholar 

  • Pham BT, Bui DT, Pourghasemi HR, Prakash I, Dholakia MB (2017) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoret Appl Climatol 128(1–2):255–273

    Article  Google Scholar 

  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016b) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environmental Modelling & Software 84:240–250

    Article  Google Scholar 

  • Pham BT, Shirzadi A, Bui DT, Prakash I, Dholakia MB (2018) 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 Sediment Res 33(2):157–170

    Article  Google Scholar 

  • Polykretis C, Chalkias C (2018) Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Nat Hazards 93(1):249–274

    Article  Google Scholar 

  • Pourghasemi H, Moradi H, Aghda SF, Gokceoglu C, Pradhan B (2014) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7(5):1857–1878

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192

    Article  Google Scholar 

  • Pradhan A, Kim Y (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. CATENA 140:125–139

    Article  Google Scholar 

  • Pradhan B, Oh HJ, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics Nat Hazards Risk 1(3):199–223

    Article  Google Scholar 

  • Qin CZ, Zhu AX, Pei T, Li BL, Scholten T, Behrens T, Zhou CH (2011) An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agric 12(1):32–43

    Article  Google Scholar 

  • Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Nat Hazards 85:1323–1346

    Article  Google Scholar 

  • Ramos-Cañón AM, Prada-Sarmiento LF, Trujillo-Vela MG, Macías JP, Santos-r AC (2016) Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá. Colombia Landslides 13(4):671–681

    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 

  • Rosa, ML (2018) Cartografia de suscetibilidade a deslizamentos utilizando o método estatístico “valor informativo”: estudo de caso na bacia do Ribeirão dos Macacos, Nova Lima/MG. Dissertation, Univesidade Federal de Ouro Preto

  • 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 

  • Sharma S, Mahajan AK (2019) A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bull Eng Geol Environ 78:2431–2448

    Article  Google Scholar 

  • Shirani K, Pasandi M, Arabameri A (2018) Landslide susceptibility assessment by dempster–shafer and index of entropy models, Sarkhoun basin, southwestern Iran. Nat Hazards 93(3):1379–1418

    Article  Google Scholar 

  • SoetersR VCJ (1996) Slope instability recognition, analysis and zonation. Landsl Investig Mitigat 247:129–177

    Google Scholar 

  • Souza ML (1996) Mapeamento geotécnico da cidade de Ouro Preto-MG (Escala 1:10000)-susceptibilidade aos movimentos de massa e processos correlatos. Dissertation,Universidade de São Paulo

  • Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Luu C (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA 188:104451

    Article  Google Scholar 

  • VanWesten CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. EngGeol 102(3–4):112–131

    Google Scholar 

  • Wilson JP, Gallant JC (2000) Terrain analysis: principles and applications, 1st edn. Wiley, Toronto

    Google Scholar 

  • Wysocki DA, Schoeneberger PJ, HirmasDR, LaGarry HE (2011) Geomorphology of soil landscapes. Handbook of Soil Sciences: Properties and Processes. 2nd edn. CRC Press, Flórida

  • Xavier, MO (2018) Mapeamento da suscetibilidade a movimentos gravitacionais de massa utilizando a análise estatística do valor informativo aplicada ao distrito sede da cidade histórica de Ouro Preto-MG.Dissertation, Univesidade Federal de Ouro Preto

  • Xi W, Li G, Moayedi H, Nguyen H (2019) A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China. Geomat Nat Hazards Risk 10(1):1750–1771

    Article  Google Scholar 

  • 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 

  • Zhu L, Huang J (2006) GIS-based logistic regression method for landslide susceptibility mapping in regional scale. J Zhejiang Univ Sci A 7(12):2007–2017

    Article  Google Scholar 

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Acknowledgements

We would like to thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior for financial support and Editage (www.editage.com) for English language editing.

Funding

This study was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

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Authors and Affiliations

  1. Geotechnics Center, Federal University of Ouro Preto, Ouro Preto, Brazil

    Cahio Guimarães Seabra Eiras, Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas

  2. Environmental Engineering Department, Federal University of Ouro Preto, Ouro Preto, Brazil

    César Falcão Barella

  3. Statistics Department, Federal University of Ouro Preto, Ouro Preto, Brazil

    Tiago Martins Pereira

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  1. Cahio Guimarães Seabra Eiras
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  2. Juliana Ribeiro Gonçalves de Souza
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Eiras, C.G.S., Souza, J.R.G., Freitas, R.D.A. et al. Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data. Nat Hazards 107, 1427–1442 (2021). https://doi.org/10.1007/s11069-021-04638-4

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