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Arabian Journal of Geosciences

, Volume 7, Issue 5, pp 1857–1878 | Cite as

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran)

  • H. R. Pourghasemi
  • H. R. MoradiEmail author
  • S. M. Fatemi Aghda
  • C. Gokceoglu
  • B. Pradhan
Original Paper

Abstract

The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Keywords

Landslide susceptibility Spatial multi-criteria evaluation Frequency ratio GIS Tehran metropolitan 

Notes

Acknowledgments

The authors gratefully acknowledge the National Geographic Organization (NGO-Iran) (http://www.ngo-iran.ir/ngo.htm) for providing the IRS satellite images. This research was carried out as part of the first author’s PhD thesis at the watershed management engineering, Tarbiat Modares University, Mazandaran, Iran. Also, the authors would like to thank two anonymous reviewers for their helpful comments on the previous version of the manuscript.

References

  1. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106Google Scholar
  2. Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34Google Scholar
  3. Akgun A, Turk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multi criteria decision analysis. Environ Earth Sci 61:595–611Google Scholar
  4. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44Google Scholar
  5. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. doi: 10.1016/j.cageo.2012.3 Google Scholar
  6. Aniya M (1985) Landslide-susceptibility mapping in the Amahata river basin, Japan. Annals Associ of American Geograph 75(1):102–114Google Scholar
  7. Atkinson PM, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology 130(1–2):55–64Google Scholar
  8. 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(1–2):15–31Google Scholar
  9. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81Google Scholar
  10. Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Math Geosci 44:47–70Google Scholar
  11. Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulas railway case study. Phys Chem Earth Parts A/B/C 35(3–5):162–171Google Scholar
  12. Beven K, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69Google Scholar
  13. Binaghi E, Luzi L, Madella P, Pergalani F, Rampini A (1998) Slope instability zonation: a comparison between certainty factor and Fuzzy Dempster–Shafer approaches. Nat Hazards 17:77–97Google Scholar
  14. Boerboom L, Flacke J, Sharifi A, Alan O (2009) Web-based spatial multi-criteria evaluation (SMCE) software, ITC Working paper 1, for the ForestClim Project 25 ppGoogle Scholar
  15. Castellanos E, Van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslide 4:311–325Google Scholar
  16. Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44(8):949–962Google Scholar
  17. Champati Ray DP, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4:101–111Google Scholar
  18. Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23Google Scholar
  19. Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472Google Scholar
  20. Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406Google Scholar
  21. Costanzo D, Rotigliano E, Irigaray C, Jimenez-Pervarez JD, Chacon J (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci 12:327–340Google Scholar
  22. Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Canadian Geotechl J38(5):911–923Google Scholar
  23. Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2012) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards. doi: 10.1007/s11069-012-0347-6
  24. Dietrich EW, Reiss R, Hsu ML, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Processes 9:383–400Google Scholar
  25. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730Google 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 75:229–250Google Scholar
  27. Ercanoglu M, Kasmer O, Temiz N (2008) Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bull Eng Geol Environ 67:565–578Google Scholar
  28. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343Google Scholar
  29. Felicisimo A, Cuartero A, Remondo J, Quiros E (2012) Mapping landslide susceptibility with logistic regression,multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. doi: 10.1007/s10346-012-0320-1
  30. Geology Survey of Iran (GSI) (1997) http://www.gsi.ir/Main/Lang_en/index.html
  31. Gokceoglu C, Sezer EA (2012) Soft computing modeling in landslide susceptibility assessment. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 51–90. doi: 10.1007/978-3-642-25495-6-2 Google Scholar
  32. Gokceoglu C, Sonmez H, Ercanoglu M (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Eng Geol 55:277–296Google Scholar
  33. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27Google Scholar
  34. Gorsevski PV, Jankowski P (2008) Discrening landslide susceptibility using rough sets. Comput Environ Urban Syst 32:53–65Google Scholar
  35. Gorsevski PV, Jankowski P, Paul PE (2006) Heuristic approach for mapping landslide hazard integrating fuzzy logic with analytic hierarchy process. Control Cybern 35(1):1–26Google Scholar
  36. Guzzetti F, Carrarra 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 31:81–216Google Scholar
  37. Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards. doi: 10.1007/s11069-012-0218-1
  38. He S, Pan P, Dai L, Wang H, Liu J (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171–172:30–41Google Scholar
  39. Hengl T, Gruber S, Shrestha DP (2003) Digital terrain analysis in ILWIS. International Institute for Geo-Information Science and Earth Observation Enschede, The Netherlands, p 62Google Scholar
  40. Herwijnen MV (1999) Spatial decision support for environmental management. Vrije Universiteit, Amsterdam, 274Google Scholar
  41. Hizbaron DR, Baiquni M, Sartohadi J, Rijanta R, Coy M (2011) Assessing social vulnerability to seismic hazard through spatial multi criteria evaluation in Bantul District, Indonesia. Conference of Development on the Margin, Tropentag 2011, 4 ppGoogle Scholar
  42. Irigaray C, Fernandez T, Hamdouni REI, Chacon J (2007) Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards 41:61–79Google Scholar
  43. I.R. of Iran Meteorological Org (IRIMO) (2011) http://www.irimo.ir/english
  44. Jenness J (2002) Surface Areas and Ratios from Elevation Grid, Jenness Enterprises, http://www.jennessent.com/arcview/ surface_areas.htm (connected: 10.08.2003)
  45. Juang CH, Lee DH, Sheu C (1992) Mapping slope failure potential using fuzzy sets. J Geotech Eng Div ASCE 118:475–493Google Scholar
  46. 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 85:347–366Google Scholar
  47. Kincal 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 59:745–756Google Scholar
  48. Komac M (2006) A landslide susceptibility model using analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74:17–28Google Scholar
  49. Kritikos T, Davies TRH (2011) GIS-based multi-criteria decision analysis for landslide susceptibility mapping at northern Evia, Greece. Z dt Ges Geowiss 162(4):421–434Google Scholar
  50. Lee S (2004) Soil erosion assessment and its verification using the universal soil loss equation and geographic information system: a case study at Boun, Korea. Environ Geol 45(4):457–465Google Scholar
  51. Lee S, Choi J, Oh H (2009) Landslide susceptibility mapping using a neuro-fuzzy. Abstract presented at American Geophysical Union, Fall Meeting 2009, abstract #NH53A-1075Google Scholar
  52. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113Google Scholar
  53. Lee S, Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672Google Scholar
  54. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41Google Scholar
  55. Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338Google Scholar
  56. Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990Google Scholar
  57. Li C, Ma T, Sun L, Li W, Zheng A (2011) Application and Verification of fractal approach to landslide susceptibility mapping. Natl Hazards. doi: 10.1007/s11069-011-9804-x
  58. Looijen JM (2010) EIA & SEA: Environmental Impact Assessment and Strategic Environmental Assessment using spatial decision support tools: distance education. ITC, Enschede, 2010Google Scholar
  59. Malczewski J (1999) GIS and multi criteria decision analysis. Wiley, New York, p 408. ISBN 978-0-471-32944-2Google Scholar
  60. Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234Google Scholar
  61. Mathew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6:17–26Google Scholar
  62. Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400Google Scholar
  63. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236Google Scholar
  64. Moore ID, Burch GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Res 22:1350–1360Google Scholar
  65. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydro Process 5:3–30Google Scholar
  66. Nafooti MH, Chabok Boldaje M(2011) Spatial prioritizing of pastures using spatial multi criteriaevaluation (Case study: Yoosef Abad watershed—Iran). 2011 2nd International Conference on Environmental Engineering and Applications IPCBEE vol. 17 (2011) IACSIT Press, Singapore, p. 4Google Scholar
  67. Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Env 58:275–287Google Scholar
  68. Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20Google Scholar
  69. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) 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 97:171–191Google Scholar
  70. Nefeslioglu, H.A., Sezer, E., Gökçeoğlu, C., Bozkır, A.S., Duman, T.Y (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering, 2010, Article ID: 901095Google Scholar
  71. Negnevitsky M (2002) Artificial intelligence—a guide to intelligent systems. Addison-Wesley Co, Great BritainGoogle Scholar
  72. Nilaweera NS, Nutalaya P (1999) Role of tree roots in slope stabilisation. Bull Eng Geol Environ 57:337–342Google Scholar
  73. Oh HJ, Lee S (2010) Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Adv 3(2):44–55Google Scholar
  74. Oh HJ, Lee S (2011) Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea. Environ Earth Sci 64(2):395–409Google Scholar
  75. 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. doi: 10.1016/j.cageo.2010.10.012 Google Scholar
  76. Okimura T, Kawatani T (1987) Mapping of the potential surface—failure sites on granite slopes. In: Gardiner E (ed) International geomorphology 1986 part I. Wiley, Chichester, pp 121–138Google Scholar
  77. Ozdemir A (2009) Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 57:1675–1686Google Scholar
  78. Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36(3–4):325–334Google Scholar
  79. Pachauri AK, Pant M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32:81–100Google Scholar
  80. Parise M (2001) Landslide mapping techniques and their use in the assessment of the landslide hazard. Phys Chem Earth 26(9):697–703Google Scholar
  81. Piegari E, Cataudella V, Di Maio R, Milano L, Nicodemi M, Soldovieri MG (2009) Electrical resistivity tomography and statistical analysis in landslide modelling: a conceptual approach. J Appl Geophysics 68(2):151–158Google Scholar
  82. Pielke RA, Schellnhuber HJ, Sahagian D (2003) Non-linearities in the earth system. Global Change News Lett 55:11–15Google Scholar
  83. Pourghasemi HR (2008) Landslide hazard assessment using fuzzy logic (Case study: a part of Haraz Watershed). A thesis presented for M.Sc. degree in Watershed Management, Faculty of Natural Resources, Department of Watershed Management, Tarbiat Modarres University, Iran (in Persian).Google Scholar
  84. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012a) Application of weights-of evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. doi: 10.1007/s12517-012-0532-7
  85. Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2012b) A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk. doi: 10.1080/19475705.2012.662915
  86. Pourghasemi HR, Mohammady M, Pradhan B (2012c) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84. doi: 10.1016/j.catena.2012.05.005 Google Scholar
  87. Pourghasemi HR, Pradhan B, Gokceoglu C (2012d) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. doi: 10.1007/s11069-012-0217-2
  88. Pourghasemi HR, Gokceoglu C, Pradhan B, Deylami Moezzi K (2012e) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Buchroithner M, Pradhan B (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49. doi: 10.1007/978-3-642-25495-6-2 Google Scholar
  89. Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C (2012) Landslide susceptibility mapping using support vector machine and GIS, J Earth Syst Sci (in press)Google Scholar
  90. Pourghasemi HR, Pradhan B, Gokceoglu C (2012g) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. Appl Mech Mater 225:486–491. doi: 10.4028/www.scientific.net/AMM.225.486 Google Scholar
  91. Pradhan B (2010a) Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45:1244–1256Google Scholar
  92. Pradhan B (2010b) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intell Syst 3:370–381Google Scholar
  93. Pradhan B (2010c) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320Google Scholar
  94. Pradhan B (2011a) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat 18(3):471–493. doi: 10.1007/s10651-010-0147-7 Google Scholar
  95. Pradhan B (2011b) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349Google Scholar
  96. Pradhan B (2011c) An assessment of the use of an advanced neural network model with five different training strategies for the preparation of landslide susceptibility maps. J Data Sci 9(1):65–81Google Scholar
  97. Pradhan B (2012) 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, doi: 10.1016/j.cageo.2012.08.023
  98. Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010a) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177Google Scholar
  99. Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model. Earth Sci Front 14(6):143–152Google Scholar
  100. 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 Geomatics 1:3–15Google Scholar
  101. Pradhan B, Lee S, Mansor S, Buchroithner MF, Jallaluddin N, Khujaimah Z (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Appl Remote Sens 2:1–11Google Scholar
  102. Pradhan B, Mansor S, Pirasteh S, Buchroithner M (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32(14):4075–4087. doi: 10.1080/01431161.2010.484433 Google Scholar
  103. Pradhan B, Pirasteh S (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Adv 3(2):26–34Google Scholar
  104. Pradhan B, Youssef AM, Varathrajoo R (2010b) Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-Spat Inf Sci 13(2):93–102Google Scholar
  105. Rahman Md R, Saha SK (2008) Remote sensing, spatial multi criteria evaluation (SMCE) and analytical hierarchy process (AHP) in optimal cropping pattern planning for a flood prone area. J Spatial Sci 53:2161–177Google Scholar
  106. Remondo J, Gonzalez A, Diaz De Teran JR, Cendrero A, Fabbri A, Cheng CF (2003) Validation of landslide susceptibility maps: examples and applications from a case study in Northern Spain. Nat Hazards 30(3):437–449Google Scholar
  107. Saaty T (1980) The analytical hierarchy Process. McGraw-Hill, New YorkGoogle Scholar
  108. Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm Eng Remote Sens 70(5):617–625Google Scholar
  109. 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):8208–8219Google Scholar
  110. Sharifi MA, Retsios V (2004) Site selection for waste disposal through spatial multiple criteria decision analysis. J Telecommun Inf Technol 3:1–11Google Scholar
  111. Sidle RC, Ochiai H (2006) Landslides: process, prediction, and land use. Water Resour Monogr Ser 18:312. doi: 10.1029/WM018
  112. Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012a) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199Google Scholar
  113. Song KY, Oh JJ, Choi J, Park I, Lee C, Lee S (2012b) Prediction of landslides using ASTER imagery and data mining models. Adv Space Res 49:978–993Google Scholar
  114. Suzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679Google Scholar
  115. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293Google Scholar
  116. Tagil S, Jenness J (2008) GIS-based automated landform classification and topographic, land cover and geologic attributes of landforms around the Yazoren Poje, Turkey. J Appl Sci 8(6):910–921Google Scholar
  117. Talebi A, Uijlenhoet R, Troch PA (2007) Soil moisture storage and hillslope stability. Nat Hazards Earth Syst Sci 7:523–534Google Scholar
  118. Tangestani MH (2009) A comparative study of Demster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: an experience from Zagros Mountains, SW Iran. J Asian Earth Sci 35:66–73Google Scholar
  119. Terlien MTJ, Van Asch TWJ, Van Westen CJ (1995) Deterministic modelling in GIS-based landslide hazard assessment. In: Carrar A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer, London, pp 57–77Google Scholar
  120. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naive Bayes models. Math Probl Eng 2012:1–26. doi: 10.1155/2012/974638 Google Scholar
  121. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2011) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro fuzzy inference system and GIS. Comput Geosci (Article on-line first available). doi: 10.1016/j.cageo.2011.10.031
  122. 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. doi: 10.1016/j.geomorph.2012.04.023, Article online first available
  123. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Spatial prediction of landslide hazards in Vietnam: a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40Google Scholar
  124. 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 36:1101–1114Google Scholar
  125. 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. 11–33Google Scholar
  126. Varnes DJ (1984) With IAEG Commission on Landslides and Other Mass Movements: landslide hazard zonations: a review of principles and practices. UNESCO, Paris, p 63Google Scholar
  127. Van Westen CJ (2012) Living with landslide risk in Europe: assessment, effects of global change, and risk management strategies, 7th Framework Program Cooperation Theme 6 Environment (including climate change) Sub-Activity 6.1.3 Natural Hazards, GIS-based training package on landslide risk assessment Work Package 7–Dissemination of project results, pp. 133Google Scholar
  128. Wan S (2012) Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping. Environ Earth Sci. doi: 10.1007/s12665-012-1832-7
  129. Wang HB, Wu SR, Shi JS, Li B (2011) Qualitative hazard and risk assessment of landslides: a practical framework for a case study in China. Nat Hazards. doi: 10.1007/s11069-011-0008-1
  130. 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 Geomorphol. doi: 10.1016/j.geomorph.2011.12.040
  131. Yalcin A (2005) An investigation on Ardesen (Rize) region on the basis of landslide susceptibility, KTU, PhD Thesis (in Turkish)Google Scholar
  132. Yalcın A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12Google Scholar
  133. 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(3):274–287Google Scholar
  134. 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
  135. Yeon YK, Han JG, Ryu KH (2012) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283Google Scholar
  136. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266Google Scholar
  137. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, pp 423.Google Scholar
  138. Yilmaz I (2009a) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68:297–306Google Scholar
  139. Yilmaz I (2009b) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138Google Scholar
  140. 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 61:821–836Google Scholar
  141. Yilmaz C, Topal T, Suzen ML (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environ Earth Sci 65:2161–2178Google Scholar
  142. Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2012) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. doi: 10.1007/s12517-012-0610-x

Copyright information

© Saudi Society for Geosciences 2013

Authors and Affiliations

  • H. R. Pourghasemi
    • 1
  • H. R. Moradi
    • 1
    Email author
  • S. M. Fatemi Aghda
    • 2
  • C. Gokceoglu
    • 3
  • B. Pradhan
    • 4
  1. 1.Department of Watershed Management Engineering, College of Natural Resources and Marine SciencesTarbiat Modares University (TMU)NoorIran
  2. 2.Department of Engineering GeologyTarbiat Moallem UniversityTehranIran
  3. 3.Applied Geology Division, Department of Geological Engineering, Engineering FacultyHacettepe UniversityAnkaraTurkey
  4. 4.Faculty of Engineering, Department of Civil EngineeringUniversity Putra MalaysiaSerdangMalaysia

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