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Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods

Abstract

Landslides constitute the most widespread and damaging natural hazards in the Constantine city. They represent a significant constraint to development and urban planning. In order to reduce the risk related to potential landslide, there is a need to develop a comprehensive landslide hazard map (LHM) of the area for an efficient disaster management and for planning development activities. The purpose of this research is to prepare and compare the LHMs of the Constantine city, by applying frequency ratio (FR), weighting factor (Wf), logistic regression (LR), weights of evidence (WOE), and analytical hierarchy process (AHP) methods used in a framework of the geographical information system (GIS). Firstly, a landslide inventory map has been prepared based on the interpretation of aerial photographs, high resolution satellite images, fieldwork, and available literature. Secondly, eight landslide-conditioning factors such as lithology, slope, exposure, rainfall, land use, distance to drainage, distance to road, and distance to fault have been considered to establish LHMs using the FR, Wf, LR, WOE, and AHP models in GIS. For verification, the obtained LHMs have been validated comparing the LHMs with the known landslide locations using the receiver operating characteristics curves (ROC). The validated results indicate that the FR method provides more accurate prediction (86.59 %) of LHMs than the WOE (82.38 %), AHP (77.86 %), Wf (77.58 %), and LR (70.45 %) models. On the other hand, the obtained results showed that all the used models in this study provided a good accuracy in predicting landslide hazard in Constantine city. The established maps can be used as useful tools for risk prevention and land use planning in the Constantine region.

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References

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

    Article  Google Scholar 

  2. ARCADIS (2003) Etude des glissements de terrain de la ville de Constantine et de ses alentours. Unpublished report

  3. Aris Y, Coiffait PE, Guiraud R (1998) Characterisation of Mesozoic–Cenozoic deformation and paleostress fields in the Central Constantinois, northeast Algeria. Tectonophysics 290:59–85

    Article  Google Scholar 

  4. Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to landsliding in the central Appennines, Italy. Comput Geosci 24(4):373–385

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-based susceptibility mapping with comparison of results from two methods and verifications. Eng Geol 81:432–445

    Article  Google Scholar 

  7. Bai S, Wang J, Lu G, Zhou P, Hou S, Xu S (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China. Geomorphology 115:23–31

    Article  Google Scholar 

  8. Barbieri G, Cambuli P (2009) The weight of evidence statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy). 18th World IMACS/MODSIM Congress, Cairns, Australia 13–17 July 2009

  9. Barredo JI, Benavides A, Hervas J, Van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int JAppl Earth Obs Geoinf 2:9–23

    Article  Google Scholar 

  10. Benaissa A, Bellouche MA (1999) Propriétés géotechniques de quelques formations géologiques propices aux glissements de terrain dans l’agglomération de Constantine (Algérie). Bull Eng Geol Environ 57:301–310

    Article  Google Scholar 

  11. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon Press, Ottawa, p 398

    Google Scholar 

  12. Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF (eds) Statistical applications in earth sciences. Geological Survey of Canada, Ottawa, pp 171–183

    Google Scholar 

  13. Bougdal R (2007) Urbanisation et mouvements de versants dans le contexte géologique et géotechnique des bassins néogènes d’Algérie du Nord. PhD thesis. USTHB, Algiers, p 185

  14. Bougdal R, Belhai D, Antoine P (2006) Géologie de la ville de Constantine et de ses environs. Bull Serv Géol Algérie 18:3–23

    Google Scholar 

  15. Bourenane H, Bouhadad Y, Guettouche MS, Braham M (2014) GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Environ. doi:10.1007/s10064-014-0616-6

    Google Scholar 

  16. Brabb EE (1984) Innovative approaches to landslide hazard and risk mapping. In: Proceedings of the fourth international symposium on landslides, vol 1. Canadian Geotechnical

  17. Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses. Geomorphology 72:250–271

    Article  Google Scholar 

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

  19. Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44:949–962

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Chung CJ, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross validation procedure. Geomorphology 94:438–452. doi:10.1016/j.geomorph.2006.12.036

    Article  Google Scholar 

  22. Crozier MJ, Glade T (2005) Landslide hazard and risk: issues, concepts and approach. In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk. Wiley, Chichester, pp 1–40

    Google Scholar 

  23. Dai FC, Lee CF, Li JXu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391

    Article  Google Scholar 

  24. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

  25. Esmali Ouri A, Amirian S (2009) Landslide hazard zonation using MR and AHP methods and GIS techniques in Langan watershed, Ardabil, Iran. International Conference on ACRS 2009, Beijing, China

  26. Gray DH, Leiser AT (1982) Biotechnical slope protection and erosion control. Van Nostrand Reinhold, New York

    Google Scholar 

  27. Guemache MA, Chatelain JL, Machane D, Benahmed S, Djadia L (2011) Failure of landslide stabilization measures: the Sidi Rached viaduct case (Constantine, Algeria). Afr Earth Sci pp 10 10.1016

  28. Guiraud R (1973) Evolution post-triasique de l’avant-pays de la chaîne alpine en Algérie, d’après l’étude du bassin du Hodna et des régions voisines. PhD. thesis. Nice Univ

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

    Article  Google Scholar 

  30. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, Oxford

    Google Scholar 

  31. Kevin LKW, Tay LT, Lateh H (2011) Landslide hazard mapping of Penang Island using probabilistic methods and logistic regression. Imaging Systems and Techniques (IST), 2011 I.E. International Conference, pp 273–278

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

    Article  Google Scholar 

  33. 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 

  34. 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):847–856

    Article  Google Scholar 

  35. Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol 44:820–833

    Article  Google Scholar 

  36. Leroi E (1996) Landslide hazard-risk maps at different scales: objectives, tools and development. In: Proc VII Int Symp Landslides, Trondheim, vol. 1. pp 35–52

  37. Letouzey J, Tremolieres P (1980) Paleo-stress fields around the Mediterranean since the Mesozoic derived from microtectonics: comparison with plate tectonic data. Mém Bur Rech Géol Min 115:261–273

    Google Scholar 

  38. Machane D, Bouhadad Y, Cheikhlounis G, Chatelain JL, Oubaiche EH, Abbes K, Guillier B, Bensalem R (2008) Examples of geomorphologic and geological hazards in Algeria. Nat Hazards 45:295–308

    Article  Google Scholar 

  39. 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(3):411–435. doi:10.1007/s11069-008-9230-x

    Article  Google Scholar 

  40. Nourani V, Komasi M, Alami M (2013) Geomorphology-based genetic programming approach for rainfall–runoff modeling. J Hydroinf 15(2):427–445

    Article  Google Scholar 

  41. Ozdemir A, Altural T (2012) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci. doi:10.1016/j.jseaes.2012.12.014

    Google Scholar 

  42. Oztekin B, Topal T (2005) GIS-based detachment susceptibility analyses of a cut slope in limestone, Ankara-Turkey. Environ Geol 49(1):124–132. doi:10.1007/s00254-005-0071-6

    Article  Google Scholar 

  43. Park S, Choi C, Kim B, Kim J (2012) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci. doi:10.1007/s12665-012-1842-5

    Google Scholar 

  44. 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(2):965–996

    Article  Google Scholar 

  45. Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS. J Earth Syst Sci 122(2):349–369

    Article  Google Scholar 

  46. Pradhan B, Lee S (2009) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. J Environ Earth Sci (2010) 60:1037–1054. Doi: 10.1007/s12665-009-0245-8

  47. Pradhan B, Lee S (2010) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  48. Pradhan B, Youssef A (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3:319–326

    Article  Google Scholar 

  49. RGPH (2008) 5ème Recensement de la Population et de l’Habitat en Algérie de l’Office National des Statistiques, (ONS, Avril 2008)

  50. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281

    Article  Google Scholar 

  51. Saaty TL (1980) The analytical hierarchy process. McGraw Hill, New York

    Google Scholar 

  52. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  53. Shahabi H, Ahmad BB, Khezri S (2012) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci. doi:10.1007/s12517-012-0650-2

    Google Scholar 

  54. Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis, and zonation. In: Turner KA, Schuster RL (eds) Landslides: investigation and mitigation. Transport Research Board Special Report, vol 247. pp 129–177

  55. Suzen ML, Doyuran V (2004) Data driven bivariate susceptibility assessment using geographical information systems: a method and application to Asatsuyu catchment, Turkey. Eng Geol 71:303–321

    Article  Google Scholar 

  56. 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. doi:10.1016/j.geomorph.2007.02.020

    Article  Google Scholar 

  57. Tien Bui D, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444

    Article  Google Scholar 

  58. Van den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410

    Article  Google Scholar 

  59. Van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation. ITC publication, vol. 15. International Institute for Aerospace and Earth Resources Survey, Enschede, p 245

  60. Van Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2.1 for windows application guide. ITC publication, Enschede, pp 73–84

    Google Scholar 

  61. 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):404–414

    Article  Google Scholar 

  62. Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  63. Vargas LG (1990) An overview of the analytic hierarchy process and its applications. Eur J Oper Res 48:2–8

    Article  Google Scholar 

  64. Varnes DJ (1978) Slope movement, types and processes. In: Schuster RL, Krizek RJ (Eds) Landslides, analyses and control. National Academy of Science, Report 176, Washington, DC, pp 11–35

  65. Varnes DJ (1984) Landslide hazard zonation, a review of principles and practice. IAEG Commission on Landslides. UNESCO, Paris. 63 pp

  66. Vila JM (1980) La chaîne alpine d’Algérie orientale et des confins algéro-tunisiens. Ph.D thesis. Paris VI Univ

  67. 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):1–12. doi:10.1016/j.catena.2007.01.003

    Article  Google Scholar 

  68. 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 85:274–287

    Article  Google Scholar 

  69. 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(3–4):251–266. doi:10.1016/j.enggeo.2005.02.002

    Article  Google Scholar 

  70. Yılmaz I (2009) 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(6):1125–1138

    Article  Google Scholar 

  71. Zhou G, Esaki T, Mitani Y, Xie M, Mori J (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386

    Article  Google Scholar 

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Acknowledgments

This research was supported by the University of USTHB (Université des Sciences et de la Technology Houari Boumediene Bab Ezzouar) of Algiers, Algeria. Authors would like to thank the Algerian Space Agency (ASAL) for providing Alsat 2A satellite imagery, the National Hydrous Resources (ANRH), and the National Office of Meteorology (ONM) for providing rainfall data. Thanks also to the DUC of Constantine (Direction de l’Urbanisme et de la Construction) and the LNHC Est (Laboratoire National Habitat et Construction) of Constantine for providing various datasets needed in this research.

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Correspondence to Hamid Bourenane.

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Bourenane, H., Guettouche, M.S., Bouhadad, Y. et al. Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods. Arab J Geosci 9, 154 (2016). https://doi.org/10.1007/s12517-015-2222-8

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Keywords

  • Landslide hazard maps
  • GIS
  • Frequency ratio
  • Weighting factor
  • Logistic regression
  • Weights of evidence
  • Analytical hierarchy process
  • Constantine
  • Algeria