Abstract
Empirical models based on machine learning methods have been used for landslide susceptibility mapping. The most accurate model is usually chosen to generate the final map. This paper demonstrates the importance of analyzing the spatial pattern of susceptibility maps, since models with similar performance can produce different output values. The relevance of terrain attributes and the sensitivity of models to input variables are also discussed. The applications of random forest (RF) and artificial neural network (ANN) models to the identification of landslide susceptible areas in the Fão River Basin, Southern Brazil, were evaluated and compared. The following have been included in the methodology: (1) the extraction of predictive attributes (e.g., slope, aspect, curvatures, valley depth) from a digital elevation model; (2) the organization of a landslide scar inventory; (3) the calibration and validation procedures of the models; (4) the analysis of model performance according to accuracy (area under the receiver operating characteristic curve) and parsimony (Akaike Information Criterion); (5) the reclassification of maps into susceptibility categories. All model configurations resulted in an accuracy above 0.9, demonstrating the ability of both techniques in landslide susceptibility mapping. The RF model stood out in this respect, recording the highest accuracy index among all tested configurations (0.949). The ANN model was more parsimonious, obtaining an accuracy of 0.925 with a much smaller number of internal connections. Thus, even with both having high and equivalent accuracy indexes, the models can establish different relationships between the input and the output susceptibility indexes, resulting in various possible landslide occurrence scenarios. These differences, together with the difficulty in defining which model presents more coherent results, reinforce the possibility of extracting spatial statistics, considering multiple configurations of models that combine accuracy and parsimony, in landslide susceptibility mapping.
Similar content being viewed by others
References
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723. https://doi.org/10.1109/tac.1974.1100705
Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci B 24(1):43–69. https://doi.org/10.1080/02626667909491834
Bonham-Carter GF (1994) Geographic information system for geoscientists: modelling with GIS. Pergamon, Oxford, p 398
BRASIL (2016) Terceira Comunicação Nacional do Brasil à Convenção-Quadro das Nações Unidas sobre Mudança do Clima – Sumário Executivo. Ministério da Ciência, Tecnologia e Inovação, Brasília
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/a:1010933404324
Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378. https://doi.org/10.1007/s10346-015-0557-6
Bulut F, Boynukalin S, Tarhan F, Ataoglu E (2000) Reliability of isopleth maps. Bull Eng Geol Environ 58:95–98
Carrara A (1983) Multivariate models for landslide hazard evolution. Math Geol 15:403–427
Chalkias C, Kalogirou S, Ferentinou M (2014) Landslide susceptibility, Peloponnese Peninsula in South Greece. J Maps 10(2):211–222. https://doi.org/10.1080/17445647.2014.884022
Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160
Cheng G, Guo L, Zhao T, Han J, Li H, Fang J (2013) Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA. Int J Remote Sens 34(1):45–59. https://doi.org/10.1080/01431161.2012.705443
Coe JA, Michael JA, Crovelli RA, Savage WA (2000) Preliminary map showing landslides densities, mean recurrence intervals, and exceedance probabilities as determined from historic records. Open-file report 00-303. USGS, Seattle
Colesanti C, Wasowski J (2006) Investigating landslides with spaceborne synthetic aperture radar (SAR) interferometry. Eng Geol 88:173–199
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. https://doi.org/10.1016/j.catena.2013.08.006
Corominas J, Van Westen CJ, 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 Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
CRED - Centre for Research on the Epidemiology of Disasters (2017) The international disaster database. http://www.emdat.be/database. Accessed 29 June 2018
Crozier MJ (1989) Landslides: causes, consequences & environment. Routledge, Croom Helm, London
Crozier MJ (2005) Multiple occurrence regional landslide events in New Zealand: hazard management issues. Landslides 2:247–256
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228. https://doi.org/10.1016/s0169-555x(01)00087-3
Dietrich WE, Reiss R, Hsu ML, Montgomery DR (1995) A process based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Process 9:383–400
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Fernandes NF, Guimarães RF, Gomes RAT, Vieira BC, Montgomery DR, Greenberg H (2001) Condicionantes Geomorfológicos dos Deslizamentos nas Encostas: Avaliação de Metodologias e Aplicação de Modelo de Previsão de Áreas Susceptíveis. Rev Bras Geomorfologia 2(1):51–71
Fernández T, Irigaray C, Hamdouni RE, Chacón J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa Area (Granada, Spain). Nat Hazards 30(3):297–308. https://doi.org/10.1023/b:nhaz.0000007092.51910.3f
Flores T, Oliveira GG (2017) Utilização de dados sub-horários de precipitação para elaboração de equação IDF para a Região do Vale do Taquari - RS. 2° Congresso de Engenharia Ambiental do Sul do Brasil. Editora UDESC, Lajes
Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72
Gitirana Jr G, Santos M, Fredlund M (2008) Three-dimensional slope stability model using finite element stress analysis. In: GeoCongress 2008, NewOrleans, LA, USA, 9–12 March 2008, pp 191–198
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
Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2):11–27
Gorsevski PV, Gessler P, Foltz RB (2000) Spatial prediction of landslide hazard using discriminant analysis and GIS. In: GIS in the Rockies 2000 Conference and Workshop: Applications for the 21st Century, Denver, CO, USA, 25–27 Sept 2000
Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415
Guha-Sapir D, Hoyois PH, Below R (2016) Annual disaster statistical review 2016: The numbers and trends. CRED, Brussels
Günther A (2003) SLOPEMAP: programs for automated mapping of geometrical and kinematical properties of hard rock hill slopes. Comput Geosci 29:865–875
Guzzetti F, Cardinali M, Reichenbach P, Carrara A (2000) Comparing landslide maps: a case study in the upper Tiber River Basin, central Italy. Environ Manag 25(3):247–363
He B, Sassa K, Nagai O, Takara K (2014) Simulation of a rapid and long-travelling landslide using 2D-RAPID and LS-RAPID 3D models. Landslide science for a safer geoenvironment. Springer, Berlin, pp 479–484
Hearn GJ (1995) Landslide and erosion hazard mapping at Ok Tedi copper mine, Papua New Guinea. Q J Eng Geol Hydroge 28(1):47–60. https://doi.org/10.1144/gsl.qjegh.1995.028.p1.05
Highland LM, Bobrowsky P (2008) The landslide handbook—a guide to understanding landslides. U.S. Geological Survey Circular 1325, Reston
IPCC (2010) Summary for policymakers. In: Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge and New York
Jaiswal P, Van Westen CJ (2009) Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology 112:96–105
Jenson SK, Domingue JO (1988) Extracting topographic structure from digital elevation data for geographic information-system analysis. Photogramm Eng Rem S 54(11):1593–1600
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3–4):347–366
Karpatne A, Ebert-Uphoff I, Ravela S, Babaie HA, Kumar V (2018) Machine learning for the geosciences: challenges and opportunities. IEEE Trans Knowl Data Eng 31(8):1544–1554
Keefer DK (2002) Investigating landslides caused by earthquakes—a historical review. Surv Geophys 23:473–510
Kim JC, Lee S, Jung HS, Lee S (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int 33(9):1000–1015. https://doi.org/10.1080/10106049.2017.1323964
Lara AA, Marques EAG, Almeida LCR (1997) Mapeamento de risco de acidentes associados a escorregamentos - Morro da Serrinha, Rio de Janeiro, Brasil. 2ª COBRAE. ABMS, ABGE e ISSMGE, Rio de Janeiro
Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geosci Front 7(1):3–10
Lazzari M, Gioia D, Anzidei B (2018) Landslide inventory of the Basilicata region (Southern Italy). J Maps 14(2):348–356. https://doi.org/10.1080/17445647.2018.1475309
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491
Lee S, Ryu J-H, Won J-S, Park H-J (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3–4):289–302
Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3–4):379–400. https://doi.org/10.1016/j.geomorph.2006.10.035
Meten M, PrakashBhandary N, Yatabe R (2015) Effect of landslide factor combinations on the prediction accuracy of landslide susceptibility maps in the Blue Nile Gorge of Central Ethiopia. Geoenviron Disasters 2(1):9. https://doi.org/10.1186/s40677-015-0016-7
Michel GP, Kobiyama M, Goerl RF (2014) Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil. J Soils Sediments 14(7):1266–1277. https://doi.org/10.1007/s11368-014-0886-4
Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46(1):33–57
Möller M, Koschitzki T, Hartmann KJ, Jahn R (2012) Plausibility test of conceptual soil maps using relief parameters. CATENA 88(1):57–67. https://doi.org/10.1016/j.catena.2011.08.002
NASA (2013) NASA shuttle radar topography mission United States 1 arc second. Version 3. NASA EOSDIS land processes DAAC, USGS earth resources observation and science (EROS) Center, Sioux Falls, South Dakota. https://lpdaac.usgs.gov. Accessed 15 July 2017
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas. USA Eng Geol 69(33):331–343
Oliveira GG, Pedrollo OC, Castro NM (2015) Simplifying artificial neural network models of river basin behaviour by an automated procedure for input variable selection. Eng Appl Artif Intel 40:47–61. https://doi.org/10.1016/j.engappai.2015.01.001
Pascale S, Parisi S, Mancini A, Schiattarella M, Conforti M, Sole A, Murgante B, Sdao F (2013) Landslide susceptibility mapping using artificial neural network in the urban area of Senise and San Costantino Albanese (Basilicata, Southern Italy). International conference on computational science and its applications. Springer, Berlin, pp 473–488. https://doi.org/10.1007/978-3-642-39649-6_34
Reid LM, Page MJ (2003) Magnitude and frequency of landsliding in a large New Zealand catchment. Geomorphology 49:71–88
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0
Samia J, Temme A, Bregt A, Wallinga J, Guzzetti F, Ardizzone F, Rossi M (2017) Characterization and quantification of path dependency in landslide susceptibility. Geomorphology 292:16–24. https://doi.org/10.1016/j.geomorph.2017.04.039
Scaioni M, Longoni L, Melillo V, Papini M, Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens 6(10):9600–9652. https://doi.org/10.3390/rs6109600
Simoni S, Zanotti F, Bertoldi G, Rigon R (2008) Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS. Hydrol Process 22:532–545
Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis and zonation. In: Turner AK, Schuster RL (eds) Landslides investigation and mitigation. TRB special report 247. National Academy Press, Washington, DC, pp 129–177
Squarzoni C, Delacourt C, Allemand P (2003) Nine years of spatial and temporal evolution of the La Vallette landslide observed by SAR interferometry. Eng Geol 68:53–66
Stead D, Eberhardt E, Coggan J, Benko B (2001) Advanced numerical techniques in rock slope stability analysis: applications and limitations. In: LANDSLIDES—causes, impacts and countermeasures, Davos, Switzerland, 17–21 June 2001, pp 615–624
Suzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment. Turk Eng Geol 71:303–321
Tseng CM, Lin CW, Fontana GD, Tarolli P (2015) The topographic signature of a major typhoon. Earth Surf Proc Land 40(8):1129–1136. https://doi.org/10.1002/esp.3708
Valadão P, Gaspar JL, Queiroz G, Ferreira T (2002) Landslides density map of S Miguel Island, Azores archipelago. Nat Hazard Earth Syst Sci 2:51–56
Van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation. Ph.D. dissertation. ITC publ. no. 15. ITC, Enschede, p 245
Widrow B, Hoff ME (1960) Adaptive Switching Circuits. IRE WESCON Convention Record, New York, pp 96–104
Wieczorek GF (1984) Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bull As Eng Geol 21(3):337–342
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856. https://doi.org/10.1007/s10346-015-0614-1
Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6(8):2873–2888. https://doi.org/10.1007/s12517-012-0610-x
Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ Earth Sci 76(11):405. https://doi.org/10.1007/s12665-017-6731-5
Acknowledgements
Special thanks go to the Research Support Foundation of Rio Grande do Sul State – FAPERGS for granting financial support to the first author (process 17/2551-0000894-4, Edict 01/2017) (newly awarded doctorate).
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.
Rights and permissions
About this article
Cite this article
de Oliveira, G.G., Ruiz, L.F.C., Guasselli, L.A. et al. Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil. Nat Hazards 99, 1049–1073 (2019). https://doi.org/10.1007/s11069-019-03795-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-019-03795-x