Natural Hazards

, 59:1413 | Cite as

Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

  • Dieu Tien BuiEmail author
  • Owe Lofman
  • Inge Revhaug
  • Oystein Dick
Original Paper


The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.


Landslide susceptibility Logistic regression Statistical index Hoa Binh province 



This research was funded by the Norwegian Quota scholarship. The data analysis and write-up were carried out as a part of the first author’s PhD studies at the Geomatics section, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Norway. I would like to thank Dr. Tran Tan Van, director of Vietnam Institute of Geosciences and Mineral Resources, for valuable comments.


  1. Atkinson PM, Massari R (1998a) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385CrossRefGoogle Scholar
  2. Atkinson PM, Massari R (1998b) Generallised linear modelling of susceptibility to landsliding in the central Apennines, Italia. Comput Geosci 24:373–385CrossRefGoogle Scholar
  3. 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):15–31. doi: 10.1016/j.geomorph.2004.06.010 CrossRefGoogle Scholar
  4. 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 115:23–31CrossRefGoogle Scholar
  5. Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralovany-Liptovsky Mikulas railway case study. Phys Chem Earth 35(3–5):162–171. doi: 10.1016/j.pce.2009.12.002 Google Scholar
  6. Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5(6):853–862CrossRefGoogle Scholar
  7. 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 72:250–271CrossRefGoogle Scholar
  8. 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):949–962. doi: 10.1007/s00254-003-0838-6 CrossRefGoogle Scholar
  9. Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472CrossRefGoogle Scholar
  10. Dai FC, Lee CF (2002) Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228CrossRefGoogle Scholar
  11. Dai FC, Lee CF, Li J, Xu JW (2001) Assessment of Landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391CrossRefGoogle Scholar
  12. Donati L, Turrini MC (2002) An objective method to rank, the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines, (Valnerina; Perugia, Italy). Eng Geol 63(3–4):277–289CrossRefGoogle Scholar
  13. Falaschi F, Giacomelli F, Federici PR, Puccinelli A, Avanzi GD, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50(3):551–569. doi: 10.1007/s11069-009-9356-5 Google Scholar
  14. Galang JS (2004) A comparison of GIS approaches instability zonation in the central Blue Ridge mountain of Virginia. Master Thesis, State University, BlacksburgGoogle Scholar
  15. Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44(1–4):147–161CrossRefGoogle Scholar
  16. 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(1–4):181–216CrossRefGoogle Scholar
  17. Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New YorkCrossRefGoogle Scholar
  18. Hue TT, Duong TV, Toan DV, Nghinh LT, Minh VC, Pho NV, Xuan PT, Hoan LT, Huyen NX, Pha PD, Chinh VV, Thom BV (2004) Investigation and assessment of the types of geological hazard in the territory of Vietnam and recommendation of remedial measures. Phase II: a study of the Northern mountainous province. Vietnam Academy of Science and Technology, Institute of Geological Sciences, HanoiGoogle Scholar
  19. Jade S, Sarkar S (1993) Statistical-models for slope instability classification. Eng Geol 36(1–2):91–98CrossRefGoogle Scholar
  20. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26(7):1477–1491. doi: 10.1080/01431160412331331012 CrossRefGoogle Scholar
  21. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113CrossRefGoogle Scholar
  22. 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):327–338. doi: 10.1007/s10346-007-0088-x CrossRefGoogle Scholar
  23. Long NT (2008) Landslide susceptibility mapping of the mountainous area in A Luoi distric, Thua Thien Hue province, Vietnam. PhD Thesis, Vrije University Brussel, BrusselGoogle Scholar
  24. 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 CrossRefGoogle Scholar
  25. Menard SW (1995) Applied logistic regression analysis. SAGE, Thousand OaksGoogle Scholar
  26. My NQ (2007) Construction of the environmental hazard zonation map for northwest territory of Vietnam. Vietnam Geography Association, HanoiGoogle Scholar
  27. Nandi A, Shakoor A (2008) Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed, Ohio, USA. Georisk 1:12Google Scholar
  28. Ohlmacher GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91(2–4):117–134. doi: 10.1016/j.enggeo.2007.01.005 CrossRefGoogle Scholar
  29. Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. doi: 10.1016/s0013-7952(03)00069-3 CrossRefGoogle Scholar
  30. 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 CrossRefGoogle Scholar
  31. Suzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679. doi: 10.1007/s00254-003-0917-8 CrossRefGoogle Scholar
  32. Thach NN, Xuan NT, My NQ, Quynh PV, Minh ND, Hoa DB, Bao DV, Dan NV, Thuy TV, Hien NT (2002) Application of remote sensing and geographical information system (GIS) for research and forecast of natural hazard in Hoa Binh province. National University Hanoi, HanoiGoogle Scholar
  33. Thinh DV, Dong NP, Hong PM, Hung PV, Khoi TN, Ke TD, Phu DV, Thang PX, Thanh PV, Thang PH, Thay BV, Thinh NT, Thien TV, Tu MT, Vinh BX (2005) The investigated report of natural hazard in the Northwest of Vietnam Northern Geological Mapping Division, HanoiGoogle Scholar
  34. Van Den Eeckhaut M, Marre A, Poesen J (2006) Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France). Geomorphology 115(1–2):141–155. doi: 10.1016/j.geomorph.2009.09.042 Google Scholar
  35. Van Westen CJ (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows Application guide. ITC Publication, EnschedeGoogle Scholar
  36. Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geologische Rundschau 86(2):404–414CrossRefGoogle Scholar
  37. Van TT, Tuy PK, Giap NX, Ke TD, Thai TN, Giang NT, Tho HM, Tuat LT, San DN, Hung LQ, Chung HT, Hoan NT (2002) Assessment and prediction of geological hazards in the 8 coastal provinces of central Vietnam from Quang Binh to Phu Yen—current status, causes, prediction and recommendation of remedial measures. Vietnam Institute of Geoscience and Mineral Resources, HanoiGoogle Scholar
  38. Van TT, Anh DT, Hieu HH, Giap NX, Ke TD, Nam TD, Ngoc D, Ngoc DTY, Thai TN, Thang DV, Tinh NV, Tuat LT, Tung NT, Tuy PK, Viet HA (2006) Investigation and assessment of the current status and potential of landslide in some sections of the Ho Chi Minh Road, National Road 1A and proposed remedial measures to prevent landslide from threat of safety of people, property, and infrastructure. Vietnam Institute of Geoscience and Mineral Resources, HanoiGoogle Scholar
  39. Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO, ParisGoogle Scholar
  40. Wang HB, Liu GJ, Xu WY, Wang GH (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29(4):548–567. doi: 10.1191/0309133305pp462ra CrossRefGoogle Scholar
  41. Wieczorek GF (1984) Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bull As Eng Geol 21(3):337–342Google Scholar
  42. 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 CrossRefGoogle Scholar
  43. 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 CrossRefGoogle Scholar
  44. Yilmaz I (2009a) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68(3):297–306. doi: 10.1007/s10064-009-0185-2 CrossRefGoogle Scholar
  45. 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(6):1125–1138. doi: 10.1016/j.cageo.2008.08.007 CrossRefGoogle Scholar
  46. Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60(3):505–519. doi: 10.1007/s12665-009-0191-5 CrossRefGoogle Scholar
  47. 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(3–4):373–386CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Dieu Tien Bui
    • 1
    Email author
  • Owe Lofman
    • 1
  • Inge Revhaug
    • 1
  • Oystein Dick
    • 1
  1. 1.Department of Mathematical Sciences and TechnologyNorwegian University of Life SciencesÅsNorway

Personalised recommendations