Abstract
The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic (ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners, able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20% of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression, linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km2 characterized by a very high density of landslides, mainly superficial slope failures triggered by intense rainfall events.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Aizerman MA, Braverman EM, Rozonoér LI (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821–837
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
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
Ballabio C (2009) Spatial prediction of soil properties in temperate mountain regions using support vector regression. Geoderma 151(3–4):338–350
Beatrizzotti G, Bellinzona G, Beltrami G, Boni A et al. (1969) Geological map of Italy fg.71-Voghera (2nd ed). Tech rep, Servizio Geologico d’Italia, Roma
Bellman R (1961) Adaptive control processes. Princeton University Press, Princeton
Bonham-Carter G (1994) Geographic information systems for geoscientists: modelling with GIS. Elsevier, London
Boulesteix AL (2007) WilcoxCV: an R package for fast variable selection in cross-validation. Methods Biochem Anal 23:1702–1704
Braga G, Braschi G, Calculli S, Caucia F et al (1985) I fenomeni franosi nell’Oltrepò Pavese: tipologia e cause. Geol Appl Idrogeol 20:621–666
Brenning A (2005) Spatial prediction models for landslide hazard: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862
CARG project (1992) The new Italian 1:50 000 geological map. Tech rep, National Geological Survey, Rome, Italy
Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Geographic information systems in assessing natural Hazards. Kluwer, Dordrecht, pp 125–175
Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Processes Landf 28:1125–1142
Carrara A, Crosta G, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94:353–378
Chen P, Lin C, Scholkopf B (2005) A tutorial on ν-support vector machines. Appl Stoch Models Bus Ind 21:111–136
Cherkassky V, Mullier F (2007) Support vector regression. In: Learning from data: concepts, theory and methods, 2ed edn. Wiley, New York, pp 439–445
Chung CJ, Fabbri A (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65:1389–1399
Chung CJ, Fabbri A (2003) Validation of spatial prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65:451–472
Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297
Cruden D, Varnes D (1996) Landslides types and processes. In: Landslides: investigation and mitigation. Transportation Research Board Special Report, vol 247. National Academy Press, Washington, pp 36–75
Dubayah R, Rich P (1995) Topographic solar radiation models for GIS. Int J Geogr Inf Syst 9:405–419
DUSAF project (2003) Destinazione d’uso dei suoli agricoli e forestali. Tech rep, Lombardy Region, Milano, Italy
Eeckhaut MVD, Vanwalleghem T, Poesen J, Govers G et al (2006) Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology 76(3–4):392–410
Egan J (1975) Signal detection theory and ROC analysis. Academic Press, New York
Evangelista P, Embrechts M, Szymanski B (2006) Taming the curse of dimensionality in kernels and novelty detection. In: Applied soft computing technologies: the challenge of complexity. Advances in Soft Computing, vol 34. Springer, Berlin, pp 425–438
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874
Fox J, Monette G (1992) Generalized collinearity diagnostics. J Am Stat Assoc 87:178–183
Goodacre C, Bonham-Carter G, Agterberg FP, Wright D (1993) A statistical analysis of spatial association of seismicity with drainage patterns and magnetic anomalies in western Quebec. Tectonophysics 217:285–305
Guyon I, Elisseeff A (2003) An introduction into variable and feature selection. J Mach Learn Res 3:1157–1182. Special Issue on Variable and Feature Selection
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422
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
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M et al (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184
Hastie T, Tibshirani R, Friedman J (2001) Local methods in high dimensions. In: The elements of statistical learning: data mining, inference and prediction. Springer, New York, pp 22–27
Hjerdt K, McDonnel J, Seibert J, Rodhe A (2004) A new topographic index to quantify downslope controls on local drainage. Water Resour Res 40:6
Hsu CW, Chang CC, Lin CJ (2007) A practical guide to support vector classification. Department of Computer Science, National Taiwan University, Taipei 106, Taiwan
Kanevski M, Pozdnoukhov A, Timonin V (2009) Machine learning for spatial environmental data. EPFL-Press
Kanevsky M, Canu S (2000) Spatial data mapping with support vector regression. Tech rep, IDIAP
LeCun Y, Denker J, Solla S, Howard R et al (1990) Optimal brain damage. In: Advances in neural information processing systems 2. Morgan Kaufmann, Denver
Lin Y, Lee Y, Wahba G (2002) Support vector machines for classification in nonstandard situations. Mach Learn 46:191–202
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60
Marquardt D (1970) Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics 12:605–607
McKenzie N, Ryan P (1999) Spatial prediction of soil properties using environmental correlation. Geoderma 89:67–94
Meisina C, Zucca F, Fossati D, Ceriani M et al (2006) Ground deformation monitoring by using the permanent scatterers technique: the example of the Oltrepo Pavese (Lombardia, Italy). Eng Geol 86(3–4):240–249
Meyer D, Leisch F, Hornik K (2003) The support vector machines under test. Neurocomputing 55:169–186
Nilsson NJ (1965) Foundations of trainable pattern classifying systems. McGraw-Hill, New York
O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690
Oommen T, Laurie G, Vogel R (2011) Sampling bias and class imbalance in maximum-likelihood logistic regression. Math Geosci 43:99–120
Platt J (1999) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Advances in large margin classifiers. MIT Press, Cambridge
Poli S, Sterlacchini S (2007) Landslide representation strategies in susceptibility studies using Weights-of-Evidence modeling technique. Nat Resour Res 16:121–134
Pozdnoukhov A, Matasci G, Kanevski M, Purves R (2011) Spatio-temporal avalanche forecasting with support vector machines. Nat Hazards Earth Syst Sci 11:367–382
Samui P, Sitharam T (2010) Applicability of statistical learning algorithms for spatial variability of rock depth. Math Geosci 42:433–446. doi:10.1007/s11004-010-9268-7
Schölkopf B (2001) The kernel trick for distances. In: Proceedings neural information processing systems, pp 301–307
Smirnoff A, Boisvert E, Paradis S (2008) Support vector machine for 3d modelling from sparse geological information of various origins. Comput Geosci 34(2):127–143
Sterlacchini S, Masetti M, Poli S (2004) Spatial integration of thematic data for predictive landslide mapping: a case study from Oltrepo Pavese area, Italy. In: Landslides: evaluation and stabilization. Taylor & Francis Group, London, pp 109–115
van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419. doi:10.1023/B:NHAZ.0000007097.42735.9e
Vapnik V (1995) The nature of statistical learning. Springer, New York
Vapnik V (1998) Statistical learning theory. Wiley, New York
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83
Yokoyama R, Shirasawa M, Pike R (2002) Visualizing topography by openness: A new application of image processing to digital elevation models. Photogramm Eng Remote Sens 68:257–265
Zevenbergen L, Thorne C (1987) Quantitative analysis of land surface topography. Earth Surf Processes Landf 12:47–56
Zhou X, Tuck D (2007) MSVM-RFE: extension of SVM-RFE for multiclass gene selection on DNA microarray data. Methods Biochem Anal 23:1106–1114
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ballabio, C., Sterlacchini, S. Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy. Math Geosci 44, 47–70 (2012). https://doi.org/10.1007/s11004-011-9379-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11004-011-9379-9