Advertisement

Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey

  • Aykut AkgunEmail author
  • Oguzhan Erkan
Original Paper

Abstract

In Turkey, landslide phenomenon is one of the most important natural hazards. Due to landslide occurrence, several landforms and man-made structures are adversely affected and may cause many injuries and loss of life. In this context, landslide susceptibility assessment is an important task to determine susceptible areas to landslide occurrence. Especially, several dam reservoir areas in Turkey are threatened by landslide phenomena. For this reason, in this study, a dam reservoir area, located in the northern part of Turkey, was selected and investigated in the point of view of landslide susceptibility assessment. A landslide susceptibility assessment for Kurtun Dam reservoir area (Gumushane, North Turkey) was carried out by geographical information system (GIS)-based statistical and deterministic models. For this purpose, logistic regression (LR) and stability index mapping (SINMAP) methodologies were applied. In this context, eight contributing factors such as altitude, lithology, slope gradient, slope aspect, distance to drainage, distance to lineament, stream power index (SPI) and topographical wetness index (TWI) were considered. After assessment of these parameters by LR and SINMAP methods in a GIS environment, two landslide susceptibility maps were obtained. Then, the produced maps were analyzed for validation purpose. For this purpose, area under curvature (AUC) approach was used. At the end of this process, the AUC values of 0.73 and 0.65 were found for LR and SINMAP models, respectively. For the performance of the SINMAP model, statistical results produced by the model were also considered. In this context, landslide density of the stability index (SI) classes were taken into account, and it was determined that 89.5 % of the landslides fall into lower and upper threshold classes which almost correspond to moderate and high susceptibility classes. These two validation values indicate that the accuracy of landslide susceptibility maps is acceptable, and the maps are feasible for further natural hazard management affairs in the area.

Keywords

Landslide susceptibility Dam reservoir GIS Deterministic Turkey 

Notes

Acknowledgments

This study was financially supported by Karadeniz Technical University, Scientific Research Projects division (project number 2008.112.005.9). The authors thank the State Hydraulics Works 22nd District Management for providing data.

References

  1. Akgun A (2011) Assessment of possible damaged areas due to landslide-induced waves at a constructed reservoir using empirical approaches: Kurtun (North Turkey) dam reservoir area. Nat Hazards Earth Syst Sci 11:1341–1350CrossRefGoogle Scholar
  2. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multicriteria decision and likelihood ratio methods: case study at Izmir, Turkey. Landslides 9(1):93–106CrossRefGoogle Scholar
  3. Akgun A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51:1377–1387CrossRefGoogle Scholar
  4. Akgun A, Turk N (2010) Landslide susceptibility mapping for ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61:595–611CrossRefGoogle Scholar
  5. Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (findikli, ne of turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54:1127–1143CrossRefGoogle Scholar
  6. Akgun A, Kıncal C, Pradhan B (2012a) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess 184(9):5453–5470CrossRefGoogle Scholar
  7. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012b) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34CrossRefGoogle Scholar
  8. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135CrossRefGoogle Scholar
  9. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36CrossRefGoogle Scholar
  10. Anderson MG, Lloyd DM (1991) Using a combined slope hydrology-stability model to develop cut slope design charts. Proc Inst Civ Eng 91:705–718Google Scholar
  11. ASTM D2216: test methods for laboratory determination of water (moisture) content of soil and rock massGoogle Scholar
  12. Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to landsliding in the central Appennines, Italy. Comput Geosci 24(4):373–385CrossRefGoogle Scholar
  13. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31CrossRefGoogle Scholar
  14. Bai SB, Wang J, Lu GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China. Geomorphology 23–31Google Scholar
  15. Baum, RL, Savage, WZ, Godt, JW (2002) TRIGRS-A fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis: U.S. geological survey open-file report 02–0424, 61 p, http://pubs.usgs.gov/of/2002/ofr-02-424/
  16. Bernknopf RL, Cambell RH, Brookshire DS, Shapiro CD (1988) A probabilistic approach to landslide hazard mapping in Cincinnati, Ohio, with applications for economic evaluation. Bull Int Assoc Eng Geol 25:39–56Google Scholar
  17. Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69CrossRefGoogle Scholar
  18. Butler DR, Walsh SJ (1990) Lithologic, structural and topographic influences on snow-avalanche path location, Eastern Glacier National Park, Montana. Ann Assoc Am Geogr 80(3):362–378CrossRefGoogle Scholar
  19. Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessment of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses. Geomorphology 72:250–271CrossRefGoogle Scholar
  20. Capparelli G, Versace P (2011) FLaIR and SUSHI: two mathematical models for early warning systems for rainfall induced landslides. Landslides 8:67–79CrossRefGoogle Scholar
  21. Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15(3):403–426CrossRefGoogle Scholar
  22. Carrara A, Cardinali M, Guzzetti F (1992) Uncertainty in assessing landslide hazard and risk. ITC J 2:172–183Google Scholar
  23. Carrara A, Cardinali M, Guzetti F, Reichenbach P (1995) GIS-based techniques for mapping landslide hazard. http://deis158.deis.unibo.it
  24. Castellanos Abella EA, Van Westen CJ (2007) Qualitative landslide susceptibility assessment by multicriteria analysis: a case study from San Antonio del Sur, Guantanamo, Cuba. Geomorphology 94(3–4):453–466Google Scholar
  25. Ceryan S, Zorlu K, Gokceoglu C, Temel A (2008) The use of cation packing index for characterizing the weathering degree of granitic rocks. Eng Geol 98:60–74CrossRefGoogle Scholar
  26. Chacon J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65:341–411CrossRefGoogle Scholar
  27. Clark WAV, Hosking PL (1986) Statistical methods for geographers. Wiley, New YorkGoogle Scholar
  28. Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364CrossRefGoogle Scholar
  29. Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961CrossRefGoogle Scholar
  30. Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250CrossRefGoogle Scholar
  31. Conoscenti C, Di Maggio C, Rotigliano E (2008) Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily. Nat Hazards 46(3):287–305CrossRefGoogle Scholar
  32. Conrad O (2002) Digitales Gelande-modell (DiGeM) terrain analysis software. http://www.geogr.unigoettingen.de/pg/saga/digem. Accessed 18.04.06
  33. Dagdelenler G, Nefeslioglu HA, Gokceoglu C (2015) Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bull Eng Geol Environ. doi: 10.1007/s10064-015-0759-0 Google Scholar
  34. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228CrossRefGoogle Scholar
  35. Demir G, Aytekin M, Akgun A, İkizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the north Anatolian fault zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65:1481–1506CrossRefGoogle Scholar
  36. Dietrich, WE, Montgomery DR (1998) SHALSTAB: a digital terrain model for mapping shallow landslide potential. Technical Report. Corvallis, OR: National Council of the Paper Industry for Air and Stream Improvement, 26 pGoogle Scholar
  37. Duman TY, Can T, Emre O, Kecer M, Dogan A, Ates S, Durmaz S (2005) Landslide inventory of Northwestern Anatolia. Eng Geol 77:99–114CrossRefGoogle Scholar
  38. Egan JP (1975) Signal detection theory and ROC analysis, Series in Cognition and Perception. Academic Press, New YorkGoogle Scholar
  39. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730CrossRefGoogle Scholar
  40. Ermini L, Filippo C, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343CrossRefGoogle Scholar
  41. Ewen JP (2000) SHETRAN: distributed river basin flow and transport modeling system. J Hydrol Eng 5:250–258CrossRefGoogle Scholar
  42. General Directorate of Mineral Research and Exploration (MTA) (2005). Geological map of Turkey,1,25.000-scaled Gumushane SheetGoogle Scholar
  43. Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81:65–83CrossRefGoogle Scholar
  44. Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415Google Scholar
  45. Gorum T, Gonencgil B, Gokceoglu C, Nefeslioglu HA (2008) Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Nat Hazards 46:323–351CrossRefGoogle Scholar
  46. Guven IH (1993) 1: 250.000 scaled geological and metallogenical map of the Eastern Black Sea Region, MTA Report (in Turkish, unpublished)Google Scholar
  47. Guzetti F, Carrarra A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy. Geomorphology 31:181–216CrossRefGoogle Scholar
  48. Hammond C, Hall D E, Miller S, Swetik P (1992) Level I stability analysis (LISA) documentation for version 2.0: U.S. Department of Agriculture, Forest Service, Intermountain Research Station; General Technical Report INT-285, Ogden, UT, 190 pGoogle Scholar
  49. Haneberg WC (2004) A rational probabilistic method for spatially distributed landslide hazard assessment. Environ Eng Geosci 10:27–43CrossRefGoogle Scholar
  50. Hosmer DW, Lomeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New YorkCrossRefGoogle Scholar
  51. Ildir B (1995) Türkiyede heyelanlarin dagilimi ve afetler yasası ile ilgili uygulamalar. In: Onalp A (ed) Proceedings of the 2nd National Landslide Symposium, Turkey, Sakarya University, pp 1–9Google Scholar
  52. Kıncal C, Akgun A, Koca MY (2009) Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59:745–756CrossRefGoogle Scholar
  53. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491CrossRefGoogle Scholar
  54. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  55. Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–219CrossRefGoogle Scholar
  56. Menard S (1995) Applied logistic regression analysis. Sage university paper series on quantitative applications in social sciences, vol. 106. Thousand Oaks, CaliforniaGoogle Scholar
  57. Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171CrossRefGoogle Scholar
  58. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydrol Process 13(4):305–320Google Scholar
  59. Moore ID, Lewis A, Gallant JC (1993) Terrain attributes: estimation methods and scale effects. In: Jakeman AJ, Beek MJ, McAleer MJ (eds) Modelling change in environmental systems. Wiley, LondonGoogle Scholar
  60. Nefeslioglu AH, Duman TY, Durmaz S (2008a) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94(3):401–418CrossRefGoogle Scholar
  61. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008b) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191CrossRefGoogle Scholar
  62. 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 1–15Google Scholar
  63. Nefeslioglu HA, Gokceoglu C, Sonmez H, Gorum T (2011) Mediumscale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey). Landslides 8(4):459–483CrossRefGoogle Scholar
  64. Nefeslioglu AH, Sezer EA, Gokceoglu C, Ayas Z (2013) A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Comput Geosci 59:1–8CrossRefGoogle Scholar
  65. O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comput Vision Graph Image Process 28(3):323–344CrossRefGoogle Scholar
  66. O’Loughlin EM (1986) Prediction of surface saturation zones in natural catchments by topographic analysis. Water Resour Res 30(4):1153–1171Google Scholar
  67. Ohlmacher CG, Davis CJ (2003) Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343CrossRefGoogle Scholar
  68. Osna T, Sezer EA, Akgun A (2014) Geofis: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30CrossRefGoogle Scholar
  69. Pack RT, Tarboton DG, Goodwin CN (1998) Terrain stability mapping with SINMAP, technical description and users guide for version 1.00. Report number 4114–0, Terratech Consulting Ltd., Salmon ArmGoogle Scholar
  70. Paulin LG, Bursik M (2009) Logisnet: a tool for multimethod, multiple soil layers slope stability analysis. Comput Geosci 35(5):1007–1016CrossRefGoogle Scholar
  71. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996CrossRefGoogle Scholar
  72. Pradhan B, Youssef AM (2009) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3(3):319–326CrossRefGoogle Scholar
  73. Pradhan B, Sezer EA, 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):4164–4177CrossRefGoogle Scholar
  74. Roodposhti MS, Rahimi S, Beglou MJ (2013) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards 73(1):77–95CrossRefGoogle Scholar
  75. Safaei M, Omar H, Huat BK, Yousuf ZBM, Ghiasi V (2011) Deterministic rainfall induced landslide approaches, advantage and limitation. Electron J Geotech Eng 16:1619–1650Google Scholar
  76. Schuster RL, Fleming RW (1986) Economic losses and fatalities due to landslides. Bull Assoc Eng Geol 23:11–28Google Scholar
  77. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219CrossRefGoogle Scholar
  78. Shaw SC, Johnson DH (1995) Slope morphology model derived from digital elevation data. Northwest Arc/Info Users Conference, Coeur d’AleneGoogle Scholar
  79. Simoni S, Zanotti F, Bertoldi G, Rigon R (2007) Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS. Hyrdrological Process 22:532–545CrossRefGoogle Scholar
  80. Soeters R, Van Westen, CJ (1996) Slope instability recognition analysis and zonation. In: Turner KT, Schuster RL (Eds) Landslides: Investigation and Mitigation. Transportation Research Board National Research Council, Washington, DC, pp. 129–177, Special Report No. 247Google Scholar
  81. Suzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey. Eng Geol 71:303–321CrossRefGoogle Scholar
  82. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–93Google Scholar
  83. Tarboton DG (1997) A new method for the determination of flow directions and contributing areas in grid digital elevation models. Water Resour Res 33(2):309–319CrossRefGoogle Scholar
  84. Terlien MT, Van Westen CJ, Van Asch TW (1995) Deterministic modelling in GIS-based landslide hazard assessment. In: Carrara A, Guzetti F (eds) Geographical information systems in assessing in natural hazards. Kluwer, The Netherlands, pp 57–77CrossRefGoogle Scholar
  85. Thiebes B (2011): Landslide analysis and early warning—local and regional case study in the Swabian Alb. PhD thesis, University of ViennaGoogle Scholar
  86. USGS (1993) USCS data user guide 5 for DEM’s, ftp://www.mapping.usgs.gov/pub/ti/DEM/demguide. Accessed 02.06.2006
  87. Van Beek LP (2002) Assessment of the influence of change. PhD thesis, Utrecht UniversityGoogle Scholar
  88. Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS based hazard zonation. Geol Rundsch 86(2):404–414CrossRefGoogle Scholar
  89. Van Westen CJ, Van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation: why is it still so difficult? Bull Eng Geol Environ 65(2):167–184CrossRefGoogle Scholar
  90. Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides analysis and control. Special Report, vol 176. Transportation Research Board, National Academy of Sciences, New York, pp 12–33Google Scholar
  91. Williams CJ, Lee SS, Fisher RA, Dickerman LH (1999) A comparison of statistical methods for prenatal screening for Down syndrome. Applied Stochastic Models and Data Analysis 15:89–101Google Scholar
  92. Wu W, Sidle R (1995) A distributed slope stability model for steep forested basins. Water Resour Res 2097–2110Google Scholar
  93. Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12CrossRefGoogle Scholar
  94. Yesilnacar EK, Topal T (2005) Landslide susceptibility mapping: comparison between logistic regression and neural networks in a medium scale study, Hendek region Turkey). Eng Geol 79:251–266CrossRefGoogle Scholar
  95. Yilmaz I (2010a) 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):821–836CrossRefGoogle Scholar
  96. Yilmaz I (2010b) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability (CP) and artificial neural networks (ANN). Environ Earth Sci 60(3):505–519CrossRefGoogle Scholar
  97. Yılmaz I, Keskin I (2009) GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ 68:459–471CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2016

Authors and Affiliations

  1. 1.Geological Engineering DepartmentKaradeniz Technical UniversityTrabzonTurkey
  2. 2.Trabzon Vacation SchoolKaradeniz Technical UniversityTrabzonTurkey

Personalised recommendations