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Estimating landslide susceptibility through a artificial neural network classifier

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Abstract

A landslide susceptibility analysis is performed through an artificial neural network (ANN) algorithm, in order to model the nonlinear relationship between landslide manifestation and geological and geomorphological parameters. The proposed methodology can be divided into two distinctive phases. In the first phase, the methodology introduces a specific distance metric, the Mahalanobis distance metric, to improve the selection of non-landslide records that “enriches” the training database and provides the model with the necessary data during the training phase. In the second phase, the methodology develops a ANN model that was capable of minimizing the effect of over-fitting by monitoring in parallel the testing data during the training phase and terminating the process of learning when a certain acceptable criteria are achieved. The model was capable in identifying unstable areas, expressed by a landslide susceptibility index. The proposed methodology has been applied in the County of Xanthi, in the northern part of Greece, an area where a well-established landslide database existed. The landslide-related parameters that had been taken in account in the analysis were the following: lithology, distance from geological boundaries, distance from tectonic features, elevation, slope inclination, slope orientation, distance from hydrographic network and distance from road network. These parameters have been normalized and reclassified and used as input variables, while the description of a given area as landslide/non-landslide was assumed to be the output variable. The final outcome of the model was a geospatial product, which expressed the landslide susceptibility index and when compared with an up-to-date landslide inventory database showed satisfactory results.

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References

  • Akgun A, Sezer EA, Nefeslioglu HA, Gockeoglu C, Pradhan B (2012) An easy to use MATLAB program (MamLand) for the assessment of landslide susceptibility using Mamdami fuzzy algorithm. Comput Geosci 38(1):23–34

    Article  Google Scholar 

  • Akinci H, Dogan S, Kiligoclu C, Temiz MS (2011) Production of landslide susceptibility map of Samsun (Turkey) city centre by using Frequency Ration Model. Int J Phys Scis 6(5):1015–1025

    Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44

    Article  Google Scholar 

  • Amari S, Murata N, Muller KR, Finke M, Yang HH (1997) Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans Neural Netw 8(5):985–996

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in Kakuda–Yahiko Mountains, central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Benardos AG, Benardos AP (2005) Applications of artificial neural networks in geotechnology. Chron Min Metall 15(1):65–81

    Google Scholar 

  • Benardos AG, Kaliampakos DC (2004) A methodology for assessing geotechnical hazards for TBM tunnelling—illustrated by Athens Metro, Greece. Int J Rock Mech Min Sci 41(4):987–999

    Article  Google Scholar 

  • Binaghi E, Luzi L, Madella P (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster–Shafer approaches. Nat Hazards 17:77–97

    Article  Google Scholar 

  • Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45:55–72

    Article  Google Scholar 

  • Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, Dordrecht, pp 135–175

    Chapter  Google Scholar 

  • Chen C, Tim US (1995) Using artificial neural networks as interpolation method in GIS. In: Proceedings of GIS/LIS. 1:157–62

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

    Article  Google Scholar 

  • Dai FC, Lee CF (2003) A spatiotemporal probabilistic modeling of storm-induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Proc Landf 28:527–545

    Article  Google Scholar 

  • Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):65–87

    Article  Google Scholar 

  • Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51:241–256

    Article  Google Scholar 

  • Elias PB, Bandis SC (2000) Neurofuzzy systems in landslide hazard assessment. In: Proceedings of 4th international symposium on spatial accuracy assessment in natural resources and environmental sciences, July 2000, pp. 199–202

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area West Black Sea region, Turkey. Eng Geol 75(3–4):229–250

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343

    Article  Google Scholar 

  • Fausett L (1994) Fundamentals of neural networks architectures, Algorithms and applications. Prentice Hall, USA, p 461

    Google Scholar 

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage W (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102:99–111

    Article  Google Scholar 

  • Ferentinou M, Sakellariou M (2007) Computational intelligence tools for the prediction of slope performance. Comput Geotech 34:362–384

    Article  Google Scholar 

  • Flentje P, Stirling D, Chowdhury R (2007) Landslide susceptibility and hazard derived from a landslide inventory using data mining—an Australian case study. In: Proceedings of the First North American Landslide Conference. Vail, Colorado, June 2007

  • Geisser S (1993) Predictive inference: an introduction. Chapman and Hall, London

    Book  Google Scholar 

  • Glade T, Anderson M, Crozier MJ (2005) Landslide hazard and risk. Wiley, Chichester, p 802

    Book  Google Scholar 

  • 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

    Article  Google Scholar 

  • Grima MA (2000) Neuro-fuzzy modelling in engineering geology. Balkema, Rotterdam, p 244

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hagan TM, Demuth BH, Beale HM (1996) Neural network design. Brooks/Cole, Electrical Engineering Series, p 730

    Google Scholar 

  • Hutchinson JN (1995) Keynote paper: landslide hazard assessment. In: Proceedings 6th International Symposium on Landslides, Christchurch. Balkema, Rotterdam, pp. 1805–1841

  • Ilia I, Tsangaratos P, Koumantakis I, Rozos D (2010) Application of a Bayesian approach in GIS-based model for evaluating landslide susceptibility. Case study Kimi area, Euboea, Greece. Bull Geol Soc Greece 3:1590–1600

    Google Scholar 

  • Ilias P, Rozos D, Konstandopoulou G, Dimadis E, Salapa E, Apostolidis E, Gemitzi A (2000) Engineering geology study of disastrous phenomena in Central Rhodope Mountain, Greek Institute of Geology and Mineral Exploration, Internal Report T-2117 (in Greek)

  • Jenness J, Brost B, Beier P (2011) Land facet corridor designer: extension for ArcGIS. Jenness Enterprises

  • 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 85:347–366

    Article  Google Scholar 

  • Kavzoglu T, Mather PM (2000) Using feature selection techniques to produce smaller neural networks with better generalization capabilities. Proc IEEE 2000 Int Geosci Rem Sens Symp Hawaii 3:3069–3071

    Google Scholar 

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the fourteenth international joint conference on artificial intelligence 2(12):1137–1143. San Mateo: Morgan Kaufmann

  • Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China. Eng Geol 76:109–128

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lee S, Pradhan B (2006) Probabilistic landslide hazard and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855

    Article  Google Scholar 

  • Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Land 27:1361–1376

    Article  Google Scholar 

  • Lee S, Choi J, Min K (2004a) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boeun, Korea. Int J Remote Sens 25:2037–2052

    Article  Google Scholar 

  • Lee S, Ryu J, Won J, Park H (2004b) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Lee CT, Huang CC, Lee JF, Pan KL, Lin ML, Dong JJ (2008) Statistical approach to earthquake-induced landslide susceptibility. Eng Geol 100:43–58

    Article  Google Scholar 

  • Lek S, Guiresse M, Giraudel JL (1999) Predicting stream nitrogen concentration from watershed features using neural network. Water Resour 33(16):3469–3478

    Google Scholar 

  • Lu P, Rosenbaum MS (2003) Artificial neural network and grey system for the prediction of slope stability. Nat Hazards 30:383–398

    Article  Google Scholar 

  • Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47:411–435

    Article  Google Scholar 

  • Marjanovic M, Bajat B, Kovaevi M (2009) Landslide susceptibility assessment with machine learning algorithms. In: Proceedings of the international conference on intelligent networking and collaborative systems, November 4–6, 2009, IEEE, Barcelona, pp. 273–278

  • McLachlan GJ (1999) Mahalanobis distance, RESONANCE, June 1999, pp. 20–26, Article accessed on 10th May 2013

  • Melchiore C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400

    Article  Google Scholar 

  • Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

    Article  Google Scholar 

  • Neaupane KM, Achet SH (2004) Use of back propagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 74(3–4):213–226

    Article  Google Scholar 

  • Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng. doi:10.1155/2010/901095, Article ID 901095

  • Neuhauser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the weights-of evidence model. Landslides 9:511–528

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276

    Article  Google Scholar 

  • Openshaw S, Openshaw C (1997) Artificial intelligence in geography. Wiley, Chichester

    Google Scholar 

  • Papazachos B, Papazachou C (1989) The earthquakes of Greece (Ziti Publ., Thessaloniki), p. 356; p. 286 (in Greek)

  • Park NW (2010) Application of Dempster–Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62(2):367–376

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural model focusing on different training sites. Int J Phys Sci 3(11):1–15

    Google Scholar 

  • Pradhan B, Lee S (2010a) Landslide susceptibility assessment and factor effect analysis: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw 25:747–759

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: a case study in Malaysia. Appl Geomat 1:3–15

    Article  Google Scholar 

  • Pradhan B, Sezer E, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177

    Article  Google Scholar 

  • Rozos D, Tsagaratos P, Markantonis K, Skias S (2006) An application of rock engineering system (RES) method for ranking the instability potential of natural slopes in Achaia County, Greece. In: Proceedings of XIth International Congress of the Society for Mathematical Geology, University of Liege, Belgium, S08–p10

  • Rozos D, Pyrgiotis L, Skias S, Tsagaratros P (2008) An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa County. Landslides 5:261–270

    Article  Google Scholar 

  • Rozos D, Barthelos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environ Earth Sci 63(1):49–63

    Article  Google Scholar 

  • Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide susceptibility zonation in Greece. Nat Hazards 65(1):523–543

    Article  Google Scholar 

  • Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi mountains, Japan. Geomorphology 109(3–4):108–121

    Article  Google Scholar 

  • Sezer AE, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  • Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng ASCE 18(2):105–114

    Article  Google Scholar 

  • Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis, and zonation. Spec Rep Natl Res Counc Transp Res Board 247:129–177

    Google Scholar 

  • Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B 36:111–147. With discussion and a reply by the authors. MR0356377

  • Sujatha ER, Rajamanickam GV, Kumaravel P (2012) Landslide susceptibility analysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed, India. J Earth Syst Sci 121(5):1337–1350

    Article  Google Scholar 

  • Tangestani MH (2009) A comparative study of Dempster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: an experience from Zagros Mountains, SW Iran. Asian J Earth Sci 35:66–73

    Article  Google Scholar 

  • Thiery Y, Malet JP, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92:38–59

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012a) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility assessment in the Hoa Binh province of Vietnam using Artificial Neural Network. Geomorphology 171–172:12–19

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Landslide susceptibility assessment at Hoa Binh province of Vietnam using an adaptive neuro fuzzy inference system and GIS. Comput Geosci 45:199–211

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012d) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Math Probl Eng 2012:1–26

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012e) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2013) Regional prediction of landslide hazard in the Hoa Binh province (Vietnam) using probability analysis of intense rainfall. Nat Hazard 60(2):707–730

    Article  Google Scholar 

  • Tsangaratos P (2012) Research on the engineering geological behaviour of the geological formations by the use of information systems. Phd Thesis, Athens, Greece, p. 363 (In Greek)

  • Tsangaratos P, Ilia I, Rozos D (2013) Case event system for landslide susceptibility analysis. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice, Springer, Berlin, Heidelberg, pp. 585–593

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36:1101–1114

    Article  Google Scholar 

  • Van Westen J, Van Asch J, Soeters R (2006) Landslide hazard and risk zonation—why is still so difficult? Bull Eng Geol Environ 65:167–184

    Article  Google Scholar 

  • Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Eng Geol 108(3–4):237–251

    Article  Google Scholar 

  • Xiang S, Nie F, Zhang C (2008) Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recogn 41:3600–3612

    Article  Google Scholar 

  • Xing EP, Ng AY, Jordan MI, Russell S (2003) Distance metric learning with application to clustering with side-information. In: Proceeding 16th Annual Advances in neural information processing systems (NIPS), Becker S, Thrun S, Obermayer, K (eds) Bradford Books, Vol 15. Cambridge, MA: MIT Press, pp. 521–528

  • Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116(3–4):274–283

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and Support Vector Machine. Environ Earth Sci 61(4):821–836

    Article  Google Scholar 

  • Yilmaz C, Topal T, Suzen ML (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak Turkey). Environ Earth Sci 65(7):2161–2178

    Article  Google Scholar 

  • Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surf Proc Land 12(1):47–56

    Article  Google Scholar 

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Acknowledgements

Authors would like to thank Dr. Thomas Glade for editorial handling, and also Prof. Pradhan and one anonymous reviewer for their helpful comments and suggestions that improved in quality the previous version of the manuscript.

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  1. Department of Geological Studies, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus: Heroon Polytechniou 9, 15780, Zografou, Greece

    Paraskevas Tsangaratos

  2. Department of Mining Engineering, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus: Heroon Polytechniou 9, 15780, Zografou, Greece

    Andreas Benardos

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Tsangaratos, P., Benardos, A. Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards 74, 1489–1516 (2014). https://doi.org/10.1007/s11069-014-1245-x

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