Arabian Journal of Geosciences

, 11:550 | Cite as

GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area, Northwest Covilhã, Portugal

  • Yacine AchourEmail author
  • Sonia Garçia
  • Victor Cavaleiro
Original Paper


Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate = 80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.


Susceptibility modeling Predisposing factors Logistic regression (LR) Frequency ratio (FR) Validation Portugal 



We are thankful to anonymous reviewers who provided many helpful comments and suggestions for improving this manuscript. We are also thankful to the centre GeoBioTec|UA (UID/GEO/04035/2013), Portugal for providing data.


  1. Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10(8):194CrossRefGoogle Scholar
  2. Aghdam IN, Pradhan B, Panahi M (2017) Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environ Earth Sci 76(6):237CrossRefGoogle Scholar
  3. Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61(3):595–611CrossRefGoogle Scholar
  4. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44CrossRefGoogle Scholar
  5. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135CrossRefGoogle Scholar
  6. Arabameri A, Pourghasemi HR, Yamani M (2017) Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci 76(24):832CrossRefGoogle Scholar
  7. 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):15–31CrossRefGoogle Scholar
  8. Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81(4):432–445CrossRefGoogle Scholar
  9. Basu T, Pal S (2017) Exploring landslide susceptible zones by analytic hierarchy process (AHP) for the Gish River basin, West Bengal, India. Spatial Information Research 25(5):665–675CrossRefGoogle Scholar
  10. Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) 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 96:28–40CrossRefGoogle Scholar
  11. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16(5):427–445CrossRefGoogle Scholar
  12. 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):949–962CrossRefGoogle Scholar
  13. 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 124:12–23CrossRefGoogle Scholar
  14. Cruden, D. M., Varnes, D. J., Turner, A. K., & Schuster, R. L. (1996). Landslides: investigation and mitigation. Special report 247. Transportation Research Board, us National Research Council, chap landslides types and processes 36-75Google Scholar
  15. Chung CJF, Fabbri AG (1993) The representation of geoscience information for data integration. Nonrenewable Resources 2(2):122–139CrossRefGoogle Scholar
  16. Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38(5):911–923CrossRefGoogle Scholar
  17. Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125CrossRefGoogle Scholar
  18. Daveau S, Ferreira AB, Ferreira N, Vieira G (1997) Novas observações sobre a glaciação da Serra da Estrela [in Portuguese]. (New observations on the Serra da Estrela glaciation). Estudos do Quaternário 1:41–51Google Scholar
  19. Dou J, Yamagishi H, Zhu Z, Yunus AP, Chen CW (2018) TXT-tool 1.081–6.1 a comparative study of the binary logistic regression (BLR) and artificial neural network (ANN) models for GIS-based spatial predicting landslides at a regional scale. In: Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools. Springer, Cham, pp 139–151CrossRefGoogle Scholar
  20. 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):229–250CrossRefGoogle Scholar
  21. Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10(5):122CrossRefGoogle Scholar
  22. Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3):85–98CrossRefGoogle Scholar
  23. García-Rodríguez MJ, Malpica JA, Benito B, Díaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95(3–4):172–191CrossRefGoogle Scholar
  24. 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(1):181–216CrossRefGoogle Scholar
  25. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299CrossRefGoogle Scholar
  26. Hadji R, Chouabi A, Gadri L, Raïs K, Hamed Y, Boumazbeur A (2016) Application of linear indexing model and GIS techniques for the slope movement susceptibility modeling in Bousselam upstream basin, Northeast Algeria. Arab J Geosci 9(3):192CrossRefGoogle Scholar
  27. Hadji R, Achour Y, Hamed Y (2017) Using GIS and RS for slope movement susceptibility mapping: comparing AHP, LI and LR methods for the Oued Mellah Basin, NE Algeria. In: Euro-Mediterranean conference for environmental integration. Springer, Cham, pp 1853–1856Google Scholar
  28. 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 171:30–41CrossRefGoogle Scholar
  29. Hungr O, Corominas J, Eberhardt E (2005) Estimating landslide motion mechanism, travel distance and velocity. Landslide risk management 1:99–128Google Scholar
  30. Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11(2):167–194CrossRefGoogle Scholar
  31. Hutchinson, M., & Gallant, J. (2000). Digital elevation models. Terrain analysis: principles and applications 29–50Google Scholar
  32. Inácio M, Soares S, Almeida P (2017) Radon concentration assessment in water sources of public drinking of Covilhã's county, Portugal. J Radiat Res Appl Sci 10(2):135–139CrossRefGoogle Scholar
  33. Jakob M, Hungr O, Jakob DM (2005) Debris-flow hazards and related phenomena, vol 739. Springer, BerlinGoogle Scholar
  34. Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics Nat Hazards Risk 9(1):49–69CrossRefGoogle Scholar
  35. Komac M, Šinigoj J, Auflič MJ (2014) A national warning system for rainfall-induced landslides in Slovenia. In: Landslide Science for a Safer Geoenvironment. Springer, Cham, pp 577–582CrossRefGoogle Scholar
  36. Kritikos T, Davies T (2015) Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western southern Alps of New Zealand. Landslides 12(6):1051–1075CrossRefGoogle Scholar
  37. Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility mapping & prediction using support vector machine for Mandakini River basin, Garhwal Himalaya, India. Geomorphology 295:115–125CrossRefGoogle Scholar
  38. Lanni C, McDonnell J, Hopp L, Rigon R (2013) Simulated effect of soil depth and bedrock topography on near-surface hydrologic response and slope stability. Earth Surf Process Landf 38(2):146–159CrossRefGoogle Scholar
  39. Larsen MC, Parks JE (1997) How wide is a road? The association of roads and mass-wasting in a forested montane environment. Earth Surf Process Landf 22(9):835–848CrossRefGoogle Scholar
  40. Lee CF, Huang WK, Chang YL, Chi SY, Liao WC (2018) Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan. Geomorphology 300:113–127CrossRefGoogle Scholar
  41. 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(7):1477–1491CrossRefGoogle Scholar
  42. Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47(7):982–990CrossRefGoogle Scholar
  43. 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):847–855CrossRefGoogle Scholar
  44. 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(4):327–338CrossRefGoogle Scholar
  45. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41CrossRefGoogle Scholar
  46. Lindsay JB (2005) The terrain analysis system: a tool for hydro-geomorphic applications. Hydrol Process 19(5):1123–1130CrossRefGoogle Scholar
  47. Melo, R., & Zêzere, J. L. (2017). Avaliação da suscetibilidade à rutura e propagação de fluxos de detritos na bacia hidrográfica do rio zêzere (serra da estrela, portugal). Revista Brasileira de Geomorfologia 18(1)Google Scholar
  48. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30CrossRefGoogle Scholar
  49. Moore ID, Grayson RB (1991) Terrain-based catchment partitioning and runoff prediction using vector elevation data. Water Resour Res 27(6):1177–1191CrossRefGoogle Scholar
  50. Nefeslioglu HA, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (eastern Black Sea region of Turkey). Geomorphology 94(3–4):401–418CrossRefGoogle Scholar
  51. 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):1264–1276CrossRefGoogle Scholar
  52. Ohlmacher GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91(2–4):117–134CrossRefGoogle Scholar
  53. Othman AA, Gloaguen R, Andreani L, Rahnama M (2015) Landslide susceptibility mapping in Mawat area, Kurdistan region, NE Iraq: a comparison of different statistical models. Natural Hazards & Earth System Sciences Discussions 3(3):1789–1833CrossRefGoogle Scholar
  54. Pham BT, Khosravi K, Prakash I (2017) Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal area, Uttarakhand, India. Environmental Processes 4(3):711–730CrossRefGoogle Scholar
  55. Pisani, B., Samper, J., & Marques, J. E. (2017). Climate change impact on groundwater resources of a hard rock mountain region (Serra da Estrela, Central Portugal). Sustain Water Res Manag, 1–16Google Scholar
  56. Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61(5):1049–1064CrossRefGoogle Scholar
  57. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365CrossRefGoogle Scholar
  58. Pourghasemi HR, Rossi M (2017) Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 130(1–2):609–633CrossRefGoogle Scholar
  59. Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. International Journal of Physical Sciences 4(1):1–15Google Scholar
  60. Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054CrossRefGoogle Scholar
  61. Pradhan B, Jebur MN (2017) Spatial prediction of landslide-prone areas through K-nearest neighbor algorithm and logistic regression model using high resolution airborne laser scanning data. In: Laser Scanning Applications in Landslide Assessment. Springer, Cham, pp 151–165CrossRefGoogle Scholar
  62. Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the Sera River basin using logistic regression model. Nat Hazards 85(3):1323–1346CrossRefGoogle Scholar
  63. Razavizadeh S, Solaimani K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76(14):499CrossRefGoogle Scholar
  64. Ribeiro, A., Munhá, J., Dias, R., Mateus, A., Pereira, E., Ribeiro, L., ... & Chaminé, H. (2007). Geodynamic evolution of the SW Europe Variscides. Tectonics 26(6)CrossRefGoogle Scholar
  65. Schleier M, Bi R, Rohn J, Ehret D, Xiang W (2014) Robust landslide susceptibility analysis by combination of frequency ratio, heuristic GIS-methods and ground truth evaluation for a mountainous study area with poor data availability in the three gorges reservoir area, PR China. Environ Earth Sci 71(7):3007–3023CrossRefGoogle Scholar
  66. 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
  67. Sharma, S., & Mahajan, A. K. (2018a). A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bull Eng Geol Environ 1–18Google Scholar
  68. Sharma S, Mahajan AK (2018b) Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India. Geoenvironmental Disasters 5(1):4CrossRefGoogle Scholar
  69. Singh K, Kumar V (2017) Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio. Arab J Geosci 10(24):539CrossRefGoogle Scholar
  70. Singh K, Kumar V (2018) Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic Chamba region in bosom of Himalaya. J Mt Sci 15(4):808–824CrossRefGoogle Scholar
  71. Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131CrossRefGoogle Scholar
  72. Varnes DJ (1978) Slope movement types and processes. Special report 176:11–33Google Scholar
  73. Vieira GT, Mora C, Ramos M (2003) Ground temperature regimes and geomorphological implications in a Mediterranean mountain (Serra da Estrela, Portugal). Geomorphology 52(1–2):57–72CrossRefGoogle Scholar
  74. Weier, J., & Herring, D. (2005). Measuring vegetation (NDVI and EVI). Earth Observatory Library of NASAGoogle Scholar
  75. 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):274–287CrossRefGoogle Scholar
  76. 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):251–266CrossRefGoogle Scholar
  77. Yilmaz 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):1125–1138CrossRefGoogle Scholar
  78. 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):821–836CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringBordj Bou Arreridj UniversityBordj Bou ArreridjAlgeria
  2. 2.Departement of Civil EngineeringBeira Interior UniversityCovilhãPortugal

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