Skip to main content
SpringerLink
Log in

Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy)

  • Technical Note
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

Active landslides are generally characterized by variations in displacement rate in response to cumulated precipitation. Velocities that are only exceeded in a limited number of days during the year might be considered as critical events, since they might determine, or prelude to, a significant evolution of the landslide. The purpose of this paper is to present a novel approach based on the use of receiver operating characteristic (ROC) curves for assessing cumulated precipitation thresholds that can provide early warning for the occurrence of critical events such as the exceedance of rare displacement rates. The approach has been developed and tested in the Piagneto landslide, an active complex rock slide—debris slide in the Northern Apennines of Italy, for which a 5-year continuous surveying monitoring dataset is available. On the basis of the first 4 years of monitoring data (training dataset), threshold curves relating cumulative precipitation (mm) to precipitation moving windows (days) have been generated by using different benchmarks that, in literature, are used to estimate the maximum predictive performance of ROC curves. These threshold curves have been successfully validated using the last 1 year of monitoring data (validation dataset). They have then been used to simulate how they might help defining different early warning levels in due advance. The proposed methodology can be replicated in any landslide for which a monitoring dataset that includes recurrent acceleration events in response to precipitation is available.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  • Abellán A, Michoud C, Jaboyedoff M, Baillifard F, Demierre J, Carrea D, Derron MH (2015) Velocity prediction on time-variant landslides using moving response functions: application to La Barmasse Rockslide (Valais, Switzerland), In: Lollino G, Giordan, D, Crosta GB, Corominas J, Azzam R, Wasowski J, Sciarra N (eds) Engineering Geology for Society and Territory - Springer International Publishing, Volume 2 SE 49. pp 323–327. doi: 10.1007/978-3-319-09057-3_49

  • Belle P, Aunay B, Bernardie S, Grandjean G, Ladouche B, Mazué R, Join JL (2014) The application of an innovative inverse model for understanding and predicting landslide movements (Salazie cirque landslides, Reunion Island). Landslides 11:343–355. doi:10.1007/s10346-013-0393-5

    Article  Google Scholar 

  • Bernardie S, Desramaut N, Malet JP, Gourlay M, Grandjean G (2014) Prediction of changes in landslide rates induced by rainfall. Landslides 12:481–494. doi:10.1007/s10346-014-0495-8

    Article  Google Scholar 

  • Bernardie S, Desramaut N, Malet JP, Azib M, Grandjean G (2015) Prediction of the rainfall-induced landslides: applications of FLAME in the French Alps. In: Lollino G, Giordan D, Crosta GB, Corominas J, Azzam R, Wasowski J, Sciarra, N (eds) Engineering Geology for Society and Territory. Springer International Publishing, Volume 2 SE 49 pp 647–651. doi:10.1007/978-3-319-09057-3_108

  • Bracegirdle A, Vaughan PR, Hight DW (1991) Displacement prediction using rate effects on residual strength. In: Bell DH (ed) Landslides, proceedings of the sixth international symposium on landslides. Christchurge. Balkema, Rotterdam, pp. 343–347

    Google Scholar 

  • Butterfield R (2000) A dynamic model of shallow slope motion driven by fluctuating ground water level. In: Bromhead E, Dixon N, Ibsen ML (eds) Landslides in research, theory and practice. Thomas Telford Ltd, London, pp. 203–208

    Google Scholar 

  • Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472. doi:10.1023/B:NHAZ.0000007172.62651.2b

    Article  Google Scholar 

  • Corominas J, Moya J, Ledesma A, Lloret A, Gili JA (2005) Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain). Landslides 2:83–96. doi:10.1007/s10346-005-0049-1

    Article  Google Scholar 

  • Corsini A, Castagnetti C, Bertacchini E, Rivola R, Ronchetti F, Capra A (2013a) Integrating airborne and multi-temporal long-range terrestrial laser scanning with total station measurements for mapping and monitoring a compound slow moving rock slide. Earth Surf Proc Land 38:1330–1338. doi:10.1002/esp.3445

    Article  Google Scholar 

  • Corsini A, Ronchetti F, Bertacchini E, Bonacini F, Calicetti P, Capra A, Castagnetti C, Piantelli E, Caputo G, Truffelli G (2013b) Large-scale slope instability affecting SS63 near the Cerreto Pass (northern Apennines, Italy). In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice risk assessment, management and mitigation, vol 6. Springer, Berlin Heidelberg, pp. 231–237. doi:10.1007/978-3-642-31319-6_32

    Google Scholar 

  • Corsini A, Mulas M, Petitta M, Bonacini F, Ronchetti F, Caputo G, Truffelli G (2015a) Prediction of landslide velocity at given cumulated rainfall values based on analysis of continuous monitoring data using ROC curves: application to the Piagneto landslide (Northern Apennine, Italy). EGU General Assembly 2015. Geophysical Research Abstracts Vol. 17, EGU2015–7264-1

  • Corsini A, Bonacini, F, Mulas, M, Petitta, M, Ronchetti, F, Truffelli, G (2015b) Long-term continuous monitoring of a deep-seated compound rock slide in the Northern Apennines (Italy), In: Lollino G, Giordan D, Crosta GB, Corominas J, Azzam R, Wasowski J, Sciarra N (eds) Engineering Geology for Society and Territory. Springer International Publishing, Volume 2 SE 49 pp 1337–1340. doi:10.1007/978-3-319-09057-3_235

  • Donner RV, Barbosa SM (2008) Nonlinear time series analysis in the geosciences: applications in climatology, geodynamics and solar-terrestrial physics. Springer, Berlin Heidelberg

    Book  Google Scholar 

  • Herrera G, Fernandez-Merodo JA, Mulas J, Pastor M, Luzi G, Monserrat O (2009) A landslide forecasting model using ground based SAR data: the Portalet case study. Eng Geol 105:220–230. doi:10.1016/j.enggeo.2009.02.009

    Article  Google Scholar 

  • Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36:1897–1910. doi:10.1029/2000WR900090

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. John Wiley & Sons, Inc, Hoboken, NJ, USA. doi:10.1002/0471722146

  • Leroueil S (2000) Contribution to the round table: peculiar aspects of structured soils. In: Evangelista A, Picarelli L (eds) The geotechnics of hard soils soft rocks: proceedings of the second international symposium on hard soils—soft rocks, Naples, Italy, 12–14 October 1998, vol 3. A. A. Balkema, Rotterdam, Netherlands, pp 1669–1677

  • Li X, Kong J, Wang Z (2012) Landslide displacement prediction based on combining method with optimal weight. Nat Hazards 61:635–646. doi:10.1007/s11069-011-0051-y

    Article  Google Scholar 

  • Li X, Kong J (2014) Application of GA–SVM method with parameter optimization for landslide development prediction. Nat Hazards Earth Syst Sci 14:525–533

    Article  Google Scholar 

  • Liu Z, Shao J, Xu W, Chen H, Shi C (2014) Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides 11:889–896. doi:10.1007/s10346-013-0443-z

    Article  Google Scholar 

  • Lollino G, Arattano M, Allasia P, Giordan D (2006) Time response of a landslide to meteorological events. Nat Hazard Earth Sci 6:179–184. doi:10.5194/nhess-6-179-2006

    Article  Google Scholar 

  • Mayoraza F, Vulliet L (2002) Neural networks for slope movement prediction. Int J Geomech 2:153–173. doi:10.1061/(ASCE)1532-3641(2002)2:2(153)

    Article  Google Scholar 

  • Moratti L and Pellegrini M (1977) Alluvioni e dissesti verificatisi nel 1972 e 1973 nei bacini dei fiumi Secchia e Panaro (Province di Modena e Reggio Emilia). Bollettino della Associazione Mineraria Sudalpina, Torino, anno XIV, n. 2, giugno 1977. 323–374

  • Nordvik T, Nyrnes E (2009) Statistical analysis of surface displacements—an example from the Aknes rockslide, western Norway. Nat Hazards Earth Syst Sci 9:713–724. doi:10.5194/nhess-9-713-2009

    Article  Google Scholar 

  • R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3–900051–07-0, URL http://www.R-project.org

  • Ronchetti F, Borgatti L, Cervi F, Lucente CC, Veneziano M, Corsini A (2007) The Valoria landslide reactivation in 2005–2006 (Northern Apennines, Italy). Landslides 4:189–195. doi:10.1007/s10346-006-0073-9

    Article  Google Scholar 

  • Schädler W, Borgatti L, Corsini A, Meier J, Ronchetti F, Schanz T (2015) Geomechanical assessment of the Corvara earthflow through numerical modelling and inverse analysis. Landslides 12:495–510. doi:10.1007/s10346-014-0498-5

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293. doi:10.1126/science.3287615

    Article  Google Scholar 

  • Zhang WJ, Chen YM, Zhan LT (2006) Loading/unloading response ratio theory applied in predicting deep-seated landslides triggering. Eng Geol 82:234–240. doi:10.1016/j.enggeo.2005.11.005

    Article  Google Scholar 

  • Vulliet L, Hutter K (1988) Viscous-type sliding laws for landslides. Can Geotech J 25:467–477

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Civil Protection Agency of the Emilia-Romagna Region under the framework agreement “Special activities on support to the forecast and emergency planning of Civil Protection with respect to hydrogeological risk” (ASPER-RER, 2011–2015). The authors wish to acknowledge Francesco Bonacini, Giuseppe Caputo, Francesco Ronchetti and Giovanni Truffelli for their collaboration in the setup and management of the RTS monitoring systems and field surveys and Marcello Petitta for some exchange of opinions on the applied methodology. The authors wish to thank Jordi Corominas (UPC, Spain) for suggesting the use of ROC curves in the analysis of the Piagneto landslide dataset.

Author information

Authors and Affiliations

  1. Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125, Modena, Italy

    Alessandro Corsini & Marco Mulas

Authors
  1. Alessandro Corsini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Mulas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Mulas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Corsini, A., Mulas, M. Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy). Landslides 14, 1241–1252 (2017). https://doi.org/10.1007/s10346-016-0781-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-016-0781-8

Keywords

Search

Navigation