Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada, Greece

  • Paraskevas Tsangaratos
  • Constantinos Loupasakis
  • Konstantinos Nikolakopoulos
  • Varvara Angelitsa
  • Ioanna Ilia
Original Article
  • 57 Downloads

Abstract

The main objective of the study was to develop a novel expert-based approach in order to construct a landslide susceptibility map for the Island of Lefkada, Greece. The developed methodology was separated into two actions. The first action involved the construction of a landslide inventory map and the second the exploitation of expert knowledge and the use of fuzzy logic to produce a landslide susceptibility map. Two types of movements were analyzed: rapid moving slides that involve rock falls and rock slides and slow to very slow moving slides. The landslide inventory map was constructed through an evaluation procedure that involved the use of a group of experts, who analyzed data acquired from remote sensing techniques supplemented by landslide records and fieldwork data. During the second action an expert-driven model was developed for identifying the tendency of landslide occurrences concerning both types of movements. A set of casual variables was selected, namely: lithological units, slope angle, slope orientation, distance from tectonic features, distance from hydrographic network and distance from road network. The performance and validation of the developed model were compared with models that are constructed on the bases of each expert’s judgment. The results proved that the most accurate and reliable outcomes are obtained from the aggregated values assigned by the group of experts and not from the individual values assigned by each expert. The area under the receiver operating characteristic curves for the models constructed by the expert’s group was 0.873 for prediction curves of rapid moving slides and 0.812 for prediction rate curves of slow to very slow moving slides, respectively. These values were much higher than those obtained by each expert. From the outcomes of the study it is clear that the produced landslide susceptibility maps could provide valuable information during landslide risk assessments at the Island of Lefkada.

Keywords

Landslide susceptibility Lefkada Greece Fuzzy logic Persistent scatterer interferometry 

Notes

Acknowledgements

The Terrafirma project has funded the SAR imagery processing as well as the geological interpretation presented in this paper. The project is one of the many services supported by the Global Monitoring for Environment and Security (GMES) Service Element Program, promoted and financed by ESA. The project is aimed at providing civil protection agencies, local authorities and disaster management organisms with support in the process of risk assessment and mitigation by using the Persistent Scatterer Interferometry. The authors gratefully acknowledge the Tele-Rilevamento Europa for having processed the SAR data.

Supplementary material

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Supplementary material 1 (RAR 1381 KB)

References

  1. Akgün A, Bulut F (2007) GIS-based landslide susceptibility for Arsin–Yomra (Trabzon, North Turkey) region. Environ Geol 51:1377–1387Google Scholar
  2. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44Google Scholar
  3. Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province. Iran Catena 120:149–162Google Scholar
  4. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in Kakuda-Yahiko Mountains, central Japan. Geomorphology 65(1–2):15–31Google Scholar
  5. Bornovas J (1964) Géologie de l’île de Lefkade. Technical Report, Geological & Geophysics Research, (I.G.S.R.) 10(1), p 142Google Scholar
  6. Bozikov J, Zaletel-Kragelj L (2010) Test validity measures and receiver operating characteristic (ROC) analysis. In: Zaletel-Kragelj L, Božikov J (eds) Methods and tools in public health, edition: public health in south-eastern Europe: a handbook for teachers, researchers and health professionals, chapter: test validity measures and receiver operating characteristic (ROC) analysis. Hans Jacobs, Lage, pp 749–770Google Scholar
  7. 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 45:55–72Google Scholar
  8. Canuti P, Casagli N, Catani F, Falorni G, Farina P (2007) Integration of remote sensing techniques in different stages of landslide response. In: Sassa K, Fukuoka H, Wang F, Wang G (eds) Progress in landslide science. Springer, Berlin, pp 251–260Google Scholar
  9. Carrara A (1983) Multivariate models for landslide hazard evaluation. J Int Assoc Math Geol 15(3):403–426Google Scholar
  10. 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. Advances in natural technological hazards research, vol 5. Springer, Dordrecht, pp 135–175Google Scholar
  11. Casson B, Baratoux D, Delacourt C, Allemand P (2003) “La Clapière” landslide motion observed from aerial differential high resolution DEM. Eng Geol 68:123–139Google Scholar
  12. Castellanos ACJ, Van Westen J (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslides 4:311–325Google Scholar
  13. Chalkias C, Polykretis C, Ferentinou M, Karymbalis E (2016) Integrating expert knowledge with statistical analysis for landslide susceptibility assessment at regional scale. Geosciences 6(1):14.  https://doi.org/10.3390/geosciences6010014 Google Scholar
  14. Champati Ray PK, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4(2):101–111Google Scholar
  15. Chou SY, Chang YH, Shen CY (2008) A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. Eur J Oper Res 189(1):132–145Google Scholar
  16. Christaras B (1997) Landslides in iliolitic and marly formations. Examples from north-western Greece. Eng Geol 47:57–69Google Scholar
  17. Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472Google Scholar
  18. Cigna F, Bianchini S, Casagli N (2013) How to assess landslide activity and intensity with persistent scatterer interferometry (PSI): the PSI-based matrix approach. Landslides 10(3):267–283Google Scholar
  19. Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88(3–4):173–199Google Scholar
  20. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Landslides, investigation and mitigation. Special report 247. Transportation Research Board, WashingtonGoogle Scholar
  21. Dai FC, Lee CF (2003) A spatiotemporal probabilistic modeling of storm-induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Process Landf 28:527–545Google Scholar
  22. Delacourt C, Allemand P, Berthier E, Raucoules D, Casson B, Grandjean P, Pambrun C, Varel E (2007) Remote-sensing techniques for analysing landslide kinematics: a review. Bull Soc Geol Fr 178(2):89–100Google Scholar
  23. Duman TY, Can T, Gokceoglu C, Nefeslioglou HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul. Turk Environ Geol 51(2):241–256Google Scholar
  24. Earthquake Planning and Protection Organization (2000) Greek seismic code (ΕΑΚ 2000, amended in 2003), Athens (in Greek) Google Scholar
  25. Ercanoglou M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64(4):949–964Google Scholar
  26. 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):229–250Google Scholar
  27. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343Google Scholar
  28. ESRI (2011) ArcGIS Desktop: release 10. Environmental Systems Research Institute, RedlandsGoogle Scholar
  29. Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128Google Scholar
  30. Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221Google Scholar
  31. 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(1–2):85–98Google Scholar
  32. Frattini P, Crosta G, Carrara A, Agliardi F (2008) Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology 94(3–4):419–437Google Scholar
  33. Ganas A, Roumelioti Z, Chousianitis K (2012) Static stress transfer from the May 20, 2012, M 6.1 Emilia-Romagna (northern Italy) earthquake using a co-seismic slip distribution model. Ann Geophys 55(4):655–662Google Scholar
  34. Ganas A, Briole P, Papathanassiou G, Bozionelos G, Avallone A, Melgar D, Argyrakis P, Valkaniotis S, Mendonidis E, Moshou A, Elias P (2015) A preliminary report on the Nov 17, 2015 M = 6.4 South Lefkada earthquake, Ionian Sea, Greece, technical report. http://www.earthquakegeology.com/materials/reports/Lefkada_17_Nov_2015-Earthquake_Report.pdf. Accessed 20 Aug 2017
  35. Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44(1–4):147–161Google Scholar
  36. Gorsevski PV, Jankowski P, Gessler PE (2006) An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35(1):121–146Google Scholar
  37. Greif V, Vlcko J (2012) Monitoring of post-failure landslide deformation by the PS-InSAR technique at Lubietova in Central Slovakia. Environ Earth Sci 66(6):1585–1595Google Scholar
  38. Guzzetti F, Cardinali M, Reichenbach P (1996) The Influence of structural setting and lithology on landslide type and pattern. Environ Eng Geosci 2(4):531–555Google Scholar
  39. 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 131:181–216Google Scholar
  40. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299Google Scholar
  41. Guzzetti F, Mondini A, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1–2):42–66Google Scholar
  42. Henriques C, Zezere JS, Marques F (2015) The role of the lithological setting on the landslide pattern and distribution. Eng Geol 189:17–31Google Scholar
  43. Hong H, Pradhan B, Xu C, Tien Bui D (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281Google Scholar
  44. Huma I, Radulescu D (1978) Automatic production of thematic maps of slope stability. Bull Int Assoc Eng Geol 17(1):95–99Google Scholar
  45. IGME (1963) Geological map of the Lefkada Island (1:50,000). Institute for Geological and Mineral Exploration, AthensGoogle Scholar
  46. Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 13(2):379–397Google Scholar
  47. Kabassi K (2009) Fuzzy simple additive weighting for evaluating a personalised geographical information system. New directions in intelligent interactive multimedia systems and services—2 volume 226 of the series studies in computational intelligence, pp 275–284Google Scholar
  48. Kaufmann A, Gupta M (1991) Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New York, p 384Google Scholar
  49. Kokinou E, Papadimitriou E, Karakostas V, Kamperis E, Vallianatos F (2006) The Kefalonia Transform Zone (offshore Western Greece) with special emphasis to its prolongation towards the Ionian Abyssal Plain. Mar Geophys Res 27(4):241–252Google Scholar
  50. Koukis G, Ziourkas C (1991) Slope instability phenomena in Greece: a statistical analysis. Bull Int Ass Eng Geol 43(1):47–60Google Scholar
  51. Koukis G, Sabatakakis N, Nikolaou N, Loupasakis C (2005) Landslides hazard zonation in Greece. In: Proceedings of open symposium on landslides risk analysis and sustainable disaster management by international consortium on landslides, Washington USA, chapter 37, pp 291–296Google Scholar
  52. Kouli M, Loupasakis C, Soupios P, Vallianatos F (2010) Landslide hazard zonation in high risk areas of Rethymnon Prefecture, Crete Island, Greece. Nat Hazards 52(3):599–621Google Scholar
  53. Kouli M, Loupasakis C, Soupios P, Rozos D, Vallianatos F (2014) Landslide susceptibility mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island, Greece. Environ Earth Sci 72(12):5197–5219Google Scholar
  54. Lee S (2004) Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environ Manag 34(2):223–232Google Scholar
  55. Lee EM, Jones DKC (2004) Landslide risk assessment. Thomas Telford, London, p 161Google Scholar
  56. Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Process Landf 28(12):1361–1376Google Scholar
  57. Lekkas E, Danamos G, Lozios S (2001) Neotectonic structure and neotectonic evolution of Lefkada Island. Bull Geol Soc Greece 34(1):157–163Google Scholar
  58. Li C, Tang H, Ge Y, Hu X, Wang L (2014) Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three Gorges Reservoir Region, China. Stoch Environ Res Risk Assess 28(6):1465–1477Google Scholar
  59. Louvari E, Kiratzi A, Papazachos B (1999) The Cephalonia Transform Fault and its extension to western Lefkada Island (Greece). Tectonophysics 308(1):223–236Google Scholar
  60. Lu P, Casagli N, Catani F, Tofani V (2012) Persistent scatterers interferometry hotspot and cluster analysis (PSI-HCA) for detection of extremely slow-moving landslides. Int J Remote Sens 33(2):466–489Google Scholar
  61. Maharaj R (1993) Landslide processes and landslide susceptibility analysis from an upland watershed: a case study from St. Andrew, Jamaica, West Indies. Eng Geol 34:53–79Google Scholar
  62. Marjanovic M, Kovaevic M, Bajat B, Vozenılek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234Google Scholar
  63. Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116:24–36Google Scholar
  64. Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400Google Scholar
  65. Metternicht G, Hurni L, Gogu R (2005) Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sen Environ 98:284–303Google Scholar
  66. 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):213–226Google Scholar
  67. 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.  https://doi.org/10.1155/2010/901095 (article ID 901095) Google Scholar
  68. Neuhauser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the weights-of evidence model. Landslides 9(4):511–528Google Scholar
  69. Nikolakopoulos GK (2012) Landslide detection using ALOS optical data. The case of Sykies Village in Andritsena, Greece. In: Proceedings of SPIE—the international society for optical engineering 10/2012.  https://doi.org/10.1117/12.975784
  70. Nikolakopoulos GK, Choussiafis C, Karathanassi V (2015) Assessing the quality of DSM from ALOS optical and radar data for automatic drainage extraction. Earth Sci Inform.  https://doi.org/10.1007/s12145-014-0199-6 Google Scholar
  71. 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):1264–1276Google Scholar
  72. Papadopoulos GA, Karastathis VK, Ganas A, Pavlides SB, Fokaefs A, Orfanogiannaki K (2003) The Lefkada, Ionian Sea (Greece), shock (Mw 6.2) of 14 August 2003: evidence for the characteristic earthquake from seismicity and ground failures. Earth Planets Space 55:713–718Google Scholar
  73. Papathanassiou G, Pavlides S, Ganas A (2005) The 2003 Lefkada earthquake: field observation and preliminary microzonation map based on liquefaction potential index for the town of Lefkada. Eng Geol 82:12–31Google Scholar
  74. Papathanassiou G, Valkaniotis S, Ganas A, Pavlides S (2013) GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the island of Lefkada, Ionian Islands, Greece. Landslides 10(6):771–783Google Scholar
  75. Papazachos B, Papazachou C (1989) The earthquakes of Greece. Ziti Publishing, Thessaloniki (in Greek) Google Scholar
  76. Parcharidis I, Foumelis M, Kourkouli P, Wegmuller U (2009) Persistent scatterers InSAR to detect ground deformation over Rio-Antirio area (Western Greece) for the period 1992–2000. J Appl Geophys 68:348–355Google Scholar
  77. Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naive bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 122:1–19.  https://doi.org/10.1007/s00704-015-1702-9 Google Scholar
  78. Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards.  https://doi.org/10.1007/s11069-016-2304-2 Google Scholar
  79. Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84Google Scholar
  80. Pourghasemi H, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996Google Scholar
  81. Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2012c) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomat Nat Hazards Risk 4:93–118Google Scholar
  82. Pourghasemi H, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province. Iran J Earth Syst Sci 122(2):349–369Google Scholar
  83. Pourghasemi H, Moradi H, Fateni Aghda S, Gokceoglu C, Pradhan B (2014) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7:1857–1878Google Scholar
  84. 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 51:350–365Google Scholar
  85. 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 Soft 25(6):747–759Google Scholar
  86. Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland. Malays Landslides 7(1):13–30Google Scholar
  87. Pradhan B, Akcapinar 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.  https://doi.org/10.1109/TGRS.2010.2050328 Google Scholar
  88. Raspini F, Loupasakis C, Rozos D, Moretti S (2013) Advanced interpretation of land subsidence by validating multi-interferometric SAR data: the case study of the Anthemountas basin (Northern Greece). Nat Hazards Earth Syst Sci 1(2):1213–1256Google Scholar
  89. Raspini F, Loupasakis C, Rozos D, Adam N, Moretti S (2014) Ground subsidence phenomena in the delta municipality region (Northern Greece): geotechnical modelling and validation with persistent scatterer interferometry. Int J Appl Earth Observ Geoinform 28:78–89Google Scholar
  90. Rondoyanni T, Sakellariou M, Baskoutas J, Christodoulou N (2012) Evaluation of active faulting and earthquake secondary effects in Lefkada Island, Ionian Sea, Greece: an overview. Nat Hazards 61:843–860Google Scholar
  91. Rondoyanni-Tsiambaou T (1997) Les seismes et l’environnement géologique de l’île de Lefkade, Grece: Passe. Eng Geol Environ Balkema.  https://doi.org/10.1088/1748-9326/7/3/035701 Google Scholar
  92. Rozos D, Pyrgiotis L, Skias S, Tsagaratos 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(3):261–270Google Scholar
  93. 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):49–63Google Scholar
  94. Ruff M, Czurda K (2008) Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology 94(3–4):314–324Google Scholar
  95. Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide susceptibility zonation in Greece. Nat Hazards 65(1):523–543Google Scholar
  96. Sachpazi M, Hirn A, Clement C, Haslinger F, Laigle M, Kissling E, Charvis P, Hello Y, Lepine JC, Sapin M, Ansorge J (2000) Western Hellenic subduction and Cephalonia Transform: local earthquakes and plate transport and strain. Tectonophysics 319:301–319Google Scholar
  97. Sakkas V, Novali F, Vassilopoulou S, Damiata BN, Fumagalli A (2014) Ground deformation of Zakynthos island (western Greece) observed by PSI and DGPS. IGARSS 2014, Quebec, Canada.  https://doi.org/10.13140/2.1.2638.4329
  98. Sarkar S, Roy A, Martha T (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. J Geol Soc India 84(4):351–362Google Scholar
  99. 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):8208–8219Google Scholar
  100. Shahabi H, Khezri S, Bin Ahmad B, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 115:55–70Google Scholar
  101. Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51(3):439–450Google Scholar
  102. 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(1):66–73Google Scholar
  103. 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 96:28–40Google Scholar
  104. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012b) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Math Probl Eng 2012:26.  https://doi.org/10.1155/2012/974638 (article ID 974638) Google Scholar
  105. 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 45:199–211Google Scholar
  106. Tofani V, Raspini F, Catani F, Casagli N (2013) Persistent scatterer interferometry (PSI) technique for landslide characterization and monitoring. Remote Sens 5(3):1045–1065Google Scholar
  107. Torizin J (2016) Elimination of informational redundancy in the weight of evidence method: an application to landslide susceptibility assessment. Stoch Environ Res Risk Assess 30(2):635–651Google Scholar
  108. Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through an artificial neural network classifier. Nat Hazards 74(3):1489–1516Google Scholar
  109. Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Prefecture. Greece Landslides 13(2):305–320Google Scholar
  110. 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(9):1101–1114Google Scholar
  111. van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rundsh 86:404–414Google Scholar
  112. Varnes DJ IAEG Commission on Landslides and other Mass-Movements (1984) Landslide hazard zonation: a review of principles and practice, UNESCO Press, ParisGoogle Scholar
  113. Wang W, Xie C, Du X (2009) Landslides susceptibility mapping based on geographical information system, GuiZhou, south-west China. Environ Geol 58(1):33–43Google Scholar
  114. Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80Google Scholar
  115. Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287Google Scholar
  116. Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582Google Scholar
  117. Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116(3–4):274–283Google Scholar
  118. 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):821–836Google Scholar
  119. Yin KL, Yan TZ (1988) Statistical prediction models for slope instability of metamorphosed rocks. In: Bonnard C (ed) Landslides, proceedings of the fifth international symposium on landslides, vol 2, Balkema, Rotterdam, pp 1269–1272Google Scholar
  120. Yiping W, Cong C, Gaofeng H, Qiuxia Z (2014) Landslide stability analysis based on random-fuzzy reliability: taking Liangshuijing landslide as a case. Stoch Environ Res Risk Assess 28(7):1723–1732Google Scholar
  121. Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth Area, Jizan, Kingdom of Saudi Arabia, by using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci.  https://doi.org/10.1007/s12665-014-4008-9 Google Scholar
  122. Youssef AM, Al-Kathery M, Pradhan B (2015a) Landslide susceptibility mapping at AlHasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19(1):113–134Google Scholar
  123. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015b) 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.  https://doi.org/10.1007/s10346-015-0614-1 Google Scholar
  124. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353Google Scholar
  125. Zhu AX, Wang R, Qiao J, Qin CZ, Chen Y, Liu J, Du F, Lin Y, Zhu T (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138Google Scholar
  126. Zimmermann HJ (2001) Fuzzy set theory—and applications, 4th rev. ed. Kluwer Academic Publishers, BostonGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical EngineeringNational Technical University of AthensAthensGreece
  2. 2.Department of Geological Division of Applied Geology and GeophysicsUniversity of PatrasPatrasGreece

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