, Volume 7, Issue 4, pp 433–444 | Cite as

Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy)

  • Federico Cervi
  • Matteo Berti
  • Lisa Borgatti
  • Francesco Ronchetti
  • Federica Manenti
  • Alessandro Corsini
Original Paper


Statistical and deterministic methods are widely used in geographic information system based landslide susceptibility mapping. This paper compares the predictive capability of three different models, namely the Weight of Evidence, the Fuzzy Logic and SHALSTAB, for producing shallow earth slide susceptibility maps, to be included as informative layers in land use planning at a local level. The test site is an area of about 450 km2 in the northern Apennines of Italy where, in April 2004, rainfall combined with snowmelt triggered hundreds of shallow earth slides that damaged roads and other infrastructure. An inventory of the landslides triggered by the event was obtained from interpretation of aerial photos dating back to May 2004. The pre-existence of mapped landslides was then checked using earlier aerial photo coverage. All the predictive models were run on the same set of geo-environmental causal factors: soil type, soil thickness, land cover, possibility of deep drainage through the bedrock, slope angle, and upslope contributing area. Model performance was assessed using a threshold-independent approach (the ROC plot). Results show that global accuracy is as high as 0.77 for both statistical models, while it is only 0.56 for SHALSTAB. Besides the limited quality of input data over large areas, the relatively poorer performance of the deterministic model maybe also due to the simplified assumptions behind the hydrological component (steady-state slope parallel flow), which can be considered unsuitable for describing the hydrologic behavior of clay slopes, that are widespread in the study area.


Shallow landslide Susceptibility Spatial analyses northern Apennines Italy 



The authors are very grateful to the Associate Editor Filippo Catani and to two anonymous reviewers, as their constructive remarks and suggestions have greatly improved the original manuscript.


  1. An P, Moon WM, Rencz A (1991) Integration of geological, geophysical, and remote sensing data using fuzzy set theory. Can J Explor Geophys 27:1–11Google Scholar
  2. Bai SB, Wang J, LÜ GN, Zhou PG, Hou SS, Xu SN (2009) GIS-based and data-driven bivariate landslide-susceptibility mapping in the three gorges area. Pedosphere 19(1):14–20CrossRefGoogle Scholar
  3. Barling RD, Moore ID, Grayson RB (1994) A quasi-dynamic wetness index for characterizing the spatial distribution of zones of surface saturation and soil water content. Water Resour Res 30:1029–1044CrossRefGoogle Scholar
  4. Baum RL, Savage WZ, Godt JW (2002) TRIGRS—a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. US Geological Survey Open-File Report 2002-0424Google Scholar
  5. Beguería S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37(3):315–329CrossRefGoogle Scholar
  6. Berti M, Simoni A. (2010) Field evidence of pore pressure diffusion in clayey soils prone to landsliding. J Geophys Res (in press)Google Scholar
  7. Boccaletti M, Elter P, Guazzone G (1971) Plate Tectonics models for the development of the Western Alps and Northern Apennines. Nat 234:108–111CrossRefGoogle Scholar
  8. Bonham-Carter GF (1994) Geographic information system for geoscientist: modelling with GIS. In: Merriam DF (ed) Computer methods in geosciences, 13. Pergamon, New York, pp 302–334Google Scholar
  9. Borga M, Dalla Fontana G, Cazorzi F (2002) Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index. J Hydrol 268:56–71CrossRefGoogle Scholar
  10. 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):427–445CrossRefGoogle Scholar
  11. Chung CF, Fabbri AG (2001) Prediction model for landslide hazard using a fuzzy set approach. In: Marchetti M, Rivas V (eds) Geomorphology and environmental impact assessment. Balkema, Rotterdam, pp 31–47Google Scholar
  12. Denison DGT, Holmes CC, Mallick BK, Smith AFM (2002) Bayesian methods for nonlinear classification and regression. Wiley, ChichesterGoogle Scholar
  13. Dietrich, WE, Sitar N (1997) Geoscience and geotechnical engineering aspects of debris-flow hazard assessment. In: Chen CL (ed) Conference presentations from 1st International Conference on Debris-Flow Hazard Mitigation: Mechanics, Prediction and Assessment, ASCE, San Francisco, USA, pp 656–676Google Scholar
  14. Dietrich WE, Montgomery DR (1998) SHALSTAB: a digital terrain model for mapping shallow landslide potential. National Council for Air and Stream ImprovementGoogle Scholar
  15. Dietrich WE, Real de Asua R, Coyle J, Orr B, Trso M (1998) A validation study of the shallow slope stability model, SHALSTAB, in forested lands of Northern California. Stillwater Ecosystem, Watershed & Riverine Sciences, BerkeleyGoogle Scholar
  16. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1/4):327–343CrossRefGoogle Scholar
  17. Fairfield J, Leymarie P (1991) Drainage networks from grid digital elevation models. Water Resour Res 27(5):709–717CrossRefGoogle Scholar
  18. Fazlagic S, Lombroso L, Quattrocchi S (2004) Osservazioni meteorologiche 2004 a Modena e a Ferrara. Atti Soc Nat Mat di Modena 135:5–40Google Scholar
  19. Guimaraes RF, Fernandes NF, Gomes RAT, Greenberg HM, Montgomery DR, Carvalho OA Jr. (2003) Parameterization of soil parameters for a model of the topographic controls on shallow landsliding. Eng Geol 69:99–108CrossRefGoogle Scholar
  20. Guthrie RH, Evans SG (2007) Work, persistence, and formative events: the geomorphic impact of landslides. Geomorphology 88:266–275CrossRefGoogle Scholar
  21. Haneberg WC (1991) Observation and analysis of pore pressure fluctuations in a thin colluvium landslide complex near Cincinnati, Ohio. Eng Geol 31:159–184CrossRefGoogle Scholar
  22. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910CrossRefGoogle Scholar
  23. Iverson RM, Major JJ (1987) Rainfall, ground-water flow, and seasonal movement at Minor Creek landslide, northwestern California: physical interpretation of empirical relations. Geol Soc Am Bulletin 99(4):579–594CrossRefGoogle Scholar
  24. Kemp LD, Bonham-Carter GF, Raines GL, Looney CG (2001) Arc-SDM: Arcview extension for spatial data modeling using weight of evidence, logistic regression, fuzzy logic and neural networks analysis. Cited 4 October 2006
  25. Kligfield R (1979) The Northern Apennines as a collisional orogen. Am J Sci 279:676–691CrossRefGoogle Scholar
  26. Lee PM (1989) Bayesian statistics: an introduction. Wiley, ChichesterGoogle Scholar
  27. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia, using frequency ratio and logistic regression models. Landslides 4(1):33–41CrossRefGoogle Scholar
  28. Leoni E (2008) Contributo della modellistica idrologica all’analisi di suscettività alle frane superficiali in argilla. PhD Thesis, University of Bologna, BolognaGoogle Scholar
  29. Looney CG (2002) Radial basis functional link nets and fuzzy reasoning. Neurocomputing 48(1/4):489–509CrossRefGoogle Scholar
  30. Looney CG, Yu H (2001) Special software development for neural network and fuzzy clustering analysis in geological information system. Geological Survey of CanadaGoogle Scholar
  31. Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control of shallow landsliding. Water Resour Res 30:1153–1171CrossRefGoogle Scholar
  32. O’Callaghan J, Mark D (1984) The extraction of drainage networks from digital elevation data. Comput Vis Gr Image Process 28(3):323–344CrossRefGoogle Scholar
  33. O’Loughlin EM (1981) Saturation regions in catchments and their relations to soil and topographic properties. J Hydrol 53(3–4):229–246CrossRefGoogle Scholar
  34. Remondo J, Bonachea J, Cendrero A (2008) Quantitative landslide risk assessment and mapping on the basis of recent occurrences. Geomorphology 94(3–4):496–507CrossRefGoogle Scholar
  35. Simoni A, Berti M (2007) Transient hydrological response of weathered clay shales and its implication for slope instability. In: Schaefer VR, Schuster RL, Turner AK (eds) (2007) Conference Presentations from 1st North American Landslide Conference, Vail, Colorado. AEG Special Publication v. 23, Association of Environmental & Engineering Geologists, Denver, CO, 802460, pp 458–471Google Scholar
  36. Simoni A, Berti M, Generali M, Elmi C, Ghirotti M (2004) Preliminary results from pore pressure monitoring on an unstable clay slope. Eng Geol 73(1–2):117–128CrossRefGoogle Scholar
  37. van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–413CrossRefGoogle Scholar
  38. van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131Google Scholar
  39. Wilcock PR, Schmidt JC, Wolman MG, Dietrich WE, DeWitt D, Doyle MW, Grant GE, Iverson RM, Montgomery DR, Pierson TC, Schilling SP, Wilson RC (2003) When models meet managers: examples from geomorphology. In: Wilcock PR, Iverson RM (eds) Prediction in Geomorphology. AGU, Washington, pp 27–40Google Scholar
  40. Zadeh LA (1988) Fuzzy logic. Computer 1(4):83–93CrossRefGoogle Scholar
  41. Zêzere JL, Trigo RM, Trigo IF (2005) Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): assessment of relationships with the North Atlantic Oscillation. Nat Hazards Earth Syst Sci 5:331–344CrossRefGoogle Scholar
  42. Zimmermann HJ (1996) Fuzzy set theory and its applications. Kluwer, BostonGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Federico Cervi
    • 1
  • Matteo Berti
    • 2
  • Lisa Borgatti
    • 3
  • Francesco Ronchetti
    • 1
  • Federica Manenti
    • 4
  • Alessandro Corsini
    • 1
  1. 1.Dipartimento di Scienze della Terra, Università di Modena e Reggio EmiliaModenaItaly
  2. 2.Dipartimento di Scienze della Terra e Geologico-AmbientaliALMA MATER STUDIORUM - Università di BolognaBolognaItaly
  3. 3.Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento, del Territorio (DISTART)ALMA MATER STUDIORUM - Università di BolognaBolognaItaly
  4. 4.Servizio Pianificazione Territoriale ed AmbientaleProvincia di Reggio EmiliaReggio EmiliaItaly

Personalised recommendations