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Journal of Mountain Science

, Volume 11, Issue 5, pp 1266–1285 | Cite as

Landslide susceptibility mapping along Bhalubang — Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models

  • Amar Deep RegmiEmail author
  • Kohki Yoshida
  • Hamid Reza Pourghasemi
  • Megh Raj DhitaL
  • Biswajeet Pradhan
Article

Abstract

Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang-Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping (LSM) of this region was carried out using frequency ratio (FR) and weights-of-evidence (W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226 (70%) were randomly selected for model training and the remaining 106 (30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified and compared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region (W of E model (success rate = 83.39%, prediction rate = 79.59%); FR model (success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.

Keywords

Landslides Frequency ratio Weights-ofevidence GIS Himalaya 

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References

  1. Akgun A (2012a) 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(1): 93–106. DOI: 10.1007/S10346-011-0283-7Google Scholar
  2. Akgun A, Sezer EA, Nefeslioglu HA, et al. (2012b) An easy-touse MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers & Geosciences 38(1):23–34. DOI: 10.1016/j.cageo.2011.04.012Google Scholar
  3. Akgun A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environmental Geology 51: 1377–1387. DOI: 10.1007/s00254-006-0435-6Google Scholar
  4. Atkinson PM, Massari R (2011) Auto logistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology 130(1–2): 55–64. DOI: 10.1016/j.geomorph.2011.02.001Google Scholar
  5. Anbalagan R (1992) Landslide hazard evaluation and zonation map-ping in mountainous terrain. Engineering Geology 32: 269–277. DOI: 10.1016/0013-7952(92)90053-2Google Scholar
  6. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS based weighted linear combination, the casein Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1): 73–81. DOI: 10.1007/s10346-003-0006-9Google Scholar
  7. Ayalew L and Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(2): 15–31. DOI: 10.1016/j.geomorph.2004.06.010Google Scholar
  8. Baeza C, Lantada N, Moya J (2010) Influence of sample and terrain unit on landslide susceptibility assessment at La Pobla de Lillet, Eastern Pyrenees, Spain. Environmental Earth Sciences 60: 155–167.Google Scholar
  9. Bai SB, Wang J, Lü G, et al. (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115: 23–31.Google Scholar
  10. 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 the Earth Sciences: Geological Survey Canada Paper 89-9, pp 171–183.Google Scholar
  11. Bonham-Carter GF (1991) Integration of geoscientific data using GIS. In: Good child MF, Rhind DW, Maguire DJ (Eds.) Geographic Information Systems: Principle and Applications. Longman, London, UK. pp 171–184.Google Scholar
  12. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. In: Bonham-Carter F (Ed.) Computer Methods in the Geosciences. Pergamon, Oxford, UK. p. 398.Google Scholar
  13. Brabb EE (1984) Innovative approaches to landslide hazard and risk mapping. Proceed. IV Int. Symp. Landslides, Toronto, 1, 307–324.Google Scholar
  14. Bui DT, Pradhan B, Lofman O, et al. (2012a) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences 45: 199–211. DOI: 10.1016/j.cageo.2011.10.031Google Scholar
  15. Bui DT, Pradhan B, Lofman O, et al. (2012b) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171–172: 12–29. DOI: 10.1016/j.geomorph.2012.04.023Google Scholar
  16. Bui DT, Pradhan B, Lofman O, et al. (2012c) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Mathematical Problems in Engineering 2012, Article ID: 974638. DOI: 10.1155/2012/974638Google Scholar
  17. Carrara A, Cardinali M, Detti R, et al. (2006). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms 16(5): 427–445. DOI: 10.1002/esp.3290160505Google Scholar
  18. Çevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology 44(8): 949–962. DOI: 10.1007/s00254-003-0838-6Google Scholar
  19. Chacon J, Irigaray C, Fernandez T, et al. (2006) Engineering geology maps: landslides and geographical information systems. Bulletin of Engineering Geology and the Environment 65: 341–411. DOI: 10.1007/s10064-006-0064-zGoogle Scholar
  20. Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli Region, Garhwal Himalayas, using Logistic Regression Model. Landslides 7(4): 411–423. DOI: 10.1007/s10346-010-0202-3Google Scholar
  21. Chung CJ, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing 65(12): 1389–1399.Google Scholar
  22. Clerici A, Perego S, Tellini C, et al. (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology 50: 941–961.Google Scholar
  23. Conforti M, Pascale S, Robustelli G, et al. (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113: 236–250. DOI: 10.1007/s00254-006-0264-7Google Scholar
  24. Corvinus G, Nanda AC (1994) Stratigraphy and palaeontology of the Siwalik Group of Surai Khola and Rato Khola in Nepal. Neues Jahrbuch fuer Geologie und Palaeontologie Abhandlungen. Marz; 1911: 25–68.Google Scholar
  25. Dahal RK, Hasegawa S, Nonomura A, et al. (2008) GIS-based weights-of-evidence modeling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology 54(2): 314–324. DOI: 10.1007/s00254-007-0818-3Google Scholar
  26. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong. Geomorphology 42: 213–228. DOI: 10.1016/S0169-555X(01)00087-3Google Scholar
  27. Devkota KC, Regmi AD, Pourghasemi HR, et al. (2013) 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. Natural Hazards (65): 1–31. DOI: 10.1007/s11069-012-0347-6Google Scholar
  28. Demir G, Aytekin M, Akgun A (2014) Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar-Resadiye (Tokat, Turkey). Arabian Journal of Geosciences. DOI 10.1007/s12517-014-1332-zGoogle Scholar
  29. Dewey JF, Cande S, Pitman III WC (1989) Tectonic evolution of the Indian/Eurasia Collision Zone. Ecolgae Geologicae Helvetiae, 82(3): 717–734. Key: citeulike:224505Google Scholar
  30. Dhital MR, Gajural AP, Pathak D, et al. (1995) Geology and structure of the Siwaliks and Lesser Himlaya in the Surai Khola-Bardanda area, Mid-Western Nepal. Bulletin of the Department of Geology, Tribhuvan University. Kathmandu, Nepal. 4: 1–70.Google Scholar
  31. 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). 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) 63(3–4): 277–289. DOI: 10.1016/S0013-7952(01)00087-4Google Scholar
  32. Ercanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environmental Earth Sciences 64(4): 949–964. DOI: 10.1007/s12665-011-0912-4Google Scholar
  33. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology 41: 720–730.Google Scholar
  34. Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology 75: 229–250. DOI: 10.1016/j.enggeo.2004.06.001Google Scholar
  35. Ercanoglu M, Gokceoglu C, van Asch TWJ (2004) Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards 32(1): 1–23. DOI: 10.1023/B:NHAZ.0000026786.85589.4aGoogle Scholar
  36. Erener A, Düzgün HSB (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7(1): 55–68. DOI: 10.1007/s10346-009-0188-xGoogle Scholar
  37. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66: 327–343. DOI: 10.1016/j.geomorph.2004.09.025Google Scholar
  38. Fell R, Corominas J, Bonnard C, et al. (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering Geology 102: 99–111. DOI: 10.1016/j.enggeo.2008.03.014Google Scholar
  39. Ganser A (1964) Geology of the Himalaya. Inter Science John Wiley, London, UK.Google Scholar
  40. 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. Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques 44: 147–161. DOI:10.1016/S0013-7952(97)81260-4Google Scholar
  41. Gokceoglu C, Sonmez H, Nefeslioglu HA, et al. (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslidesusceptibility map of its near vicinity. Engineering Geology 81: 65–83. DOI: 10.1016/j.enggeo.2005.07.011Google Scholar
  42. Goretti KKM (2010) Landslide occurrence in the hilly areas of Bududa Districts in Eastern Uganda and their causes. Unpublished PhD thesis, Makerere University, Uganda. p. 106.Google Scholar
  43. Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Computers & Geosciences 36: 1005–1020. DOI: 10.1016/j.cageo.2010.03.001Google Scholar
  44. Guzzetti F, Carrara A, Cardinali M, et al. (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31: 181–216. DOI: 10.1016/S0169-555X(99)00078-1Google Scholar
  45. Guzzetti F, Reinchenbach P, Cardinali M, et al. (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72: 272–299. DOI: 10.1016/j.geomorph.2005.06.002Google Scholar
  46. Guzzetti F, Reichenbach P, Ardizzone F, et al. (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81: 166–184. DOI: 10.1016/j.geomorph.2006.04.007Google Scholar
  47. Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Natural Hazards 63(2): 1157–1179. DOI: 10.1007/s11069-012-0218-1Google Scholar
  48. Hutchinson JN (1992) Landslide Hazard Assessment. Proceedings of the 6th International Symposium on Landslides, Christchurch, New Zealand. 3: 3–35.Google Scholar
  49. Jaafari A, Najafi A, Pourghasemi HR, et al. (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology 11(4): 909–926. DOI:10.1007/s13762-013-0464-0Google Scholar
  50. Juang CH, Lee DH, Sheu C (1992). Mapping slope failure potential using fuzzy sets. Journal of Geotechnical Engineering, 118(3): 475–494. DOI: 10.1061/(ASCE)0733-9410(1992)118:3(475)Google Scholar
  51. Kayastha P, Dhital MR, Smedt F De (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Computers and Geosciences, 52:398–408. DOI: 10.1016/j.cageo.2012.11.003Google Scholar
  52. Kanungo DP, Arora MK, Sarkar S, et al. (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology 85: 347–366. DOI: 10.1016/j.enggeo.2006.03.004Google Scholar
  53. Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine Slovenia. Geomorphology 71: 17–28. DOI: 10.1016/j.geomorph.2005.07.005Google Scholar
  54. Lee EM, Jones DKC (2004) Landslide risk assessment. Thomas Telford, London, UK. p 454.Google Scholar
  55. Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science 115(6): 661–672. DOI: 10.1007/s12040-006-0004-0Google Scholar
  56. Lee S, Kim YS, Oh HJ (2012) Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. Journal of Environmental Management 96: 91–105. DOI:10.1016/j.jenvman.2011.09.016Google Scholar
  57. Lee S, Min K (2001) Statistical analyses of landslide susceptibility at Yongin, Korea. Environmental Geology 40: 1095–1113. DOI: 10.1007/s002540100310Google Scholar
  58. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4: 33–41. DOI: 10.1007/s10346-006-0047-yGoogle Scholar
  59. Mancini F, Ceppi C, Ritrovato G (2010) GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Natural Hazards & Earth System Sciences 10: 1851–1864. DOI: 10.5194/nhess-10-1851-2010Google Scholar
  60. Melchiorre C, Matteucci M, Azzoni A, et al. (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94: 379–400. DOI: 10.1016/j.geomorph.2006.10.035Google Scholar
  61. 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. Journal of Asian Earth Sciences 61: 221–236. DOI: 10.1016/j.jseaes.2012.10.005Google Scholar
  62. Moore ID, Grayson RB (1991) Terrain-based catchment partitioning and runoff prediction using vector elevation data. Water Resources Research 27(6): 1171–1191. DOI: 10.1029/91WR00090Google Scholar
  63. Muthu K, Petrou M, Tarantino C, et al. (2008) Landslide possibility mapping using fuzzy approaches. IEEE Transactions on Geoscience and Remote Sensing 46(4): 1253–1265. DOI: 10.1109/TGRS.2007.912441Google Scholar
  64. Nefeslioglu HA, Sezer E, Gokceoglu C, et al. (2010) Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey. Mathematical Problems in Engineering 2010: 1–16. DOI: 10.1155/2010/901095.Google Scholar
  65. Nefeslioğlu 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. Engineering Geology 97(3/4): 171–191. DOI: 10.1016/j.enggeo.2008.01.004Google Scholar
  66. Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86: 12–24. DOI: 10.1016/j.geomorph.2006.08.002Google Scholar
  67. Oh HJ, Lee S (2011) Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea. Environmental Earth Sciences 64(2): 395–409. DOI: 10.1007/s12665-010-0864-0Google Scholar
  68. Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences 64: 180–197. DOI: DOI: 10.1016/j.jseaes.2012.12.014Google Scholar
  69. Pachauri AK, Gupta PV, Chander R (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology 36(3–4): 325–334. DOI: 10.1007/s002540050348Google Scholar
  70. Pachauri AK, Pant M (1992) Landslide hazard mapping based on geological attributes. Engineering Geology 32: 81–100. DOI: 10.1016/0013-7952(92)90020-YGoogle Scholar
  71. Peng L, Niu R, Huang B, et al. (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology 204: 287–301. DOI: 10.1016/j.geomorph. 2013.08.013Google Scholar
  72. Pellicani R, van Westen CJ, Spilotro G (2014) Assessing landslide exposure in areas with limited landslide information. Landslides 11(3):463–480. DOI: 10.1007/s10346 -013-0386-4Google Scholar
  73. Poudyal CP, Chang C, Oh H-J, et al. (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences 61: 1049–1064. DOI: 10.1007/s12665-009-0426-5Google Scholar
  74. Pourghasemi HR, Moradi HR, Aghda SMF, et al. (2011) GIS based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arabian Journal of Geosciences 7(5): 1857–1878. DOI: 10.1007/s12517-012-0825-xGoogle Scholar
  75. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards 63(2): 965–996. DOI: 10.1007/s11069-012-0217-2Google Scholar
  76. Pourghasemi HR, Pradhan B, Gokceoglu C, et al. (2013) A comparative assessment of prediction capabilities of Dempster-Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics, Natural Hazards and Risk 4(2): 93–118. DOI: 10.1080/19475705.2012.662915Google Scholar
  77. Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Science 4: 1–15.Google Scholar
  78. Pradhan B (2010) Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems 3: 370–381. DOI: 10.1080/18756891.2010.9727707Google Scholar
  79. 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. Environmental Earth Sciences 60: 1037–1054. DOI: 10.1007/s12665-009-0245-8Google Scholar
  80. Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Sciences 63(2): 329–349. DOI: 10.1007/s12665-010-0705-1Google Scholar
  81. 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. Computers & Geosciences 51: 350–365. DOI: 10.1016/j.cageo.2012.08.023Google Scholar
  82. Rajbhandari PCL, Alam BM, Akther MS (2002) Application of GIS (Geographic Information System) for landslide hazard zonation and mapping disaster prone area: a study of Kulekhani Watershed, Nepal. Plan plus 1(1): 117–123.Google Scholar
  83. Regmi AD, Yoshida K, Nagata H, et al. (2013) The relationship between geology and rock weathering on the rock instability along Mugling-Narayanghat road corridor, Central Nepal Himalaya. Natural Hazards 66(2): 501–532. DOI: 10.1007/s11069-012-0497-6Google Scholar
  84. Regmi AD, Devkota KC, Yoshida K, et al. (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences 7(2): 725–742. DOI: 10.1007/s12517-012-0807-zGoogle Scholar
  85. Regmi NR, Giardino JR, Vitek JD (2010a) Assessing susceptibility to landslides: using models to understand observed changes in slopes. Geomorphology 112: 25–38. DOI: 10.1016/j.geomorph.2010.05.009Google Scholar
  86. Regmi NR, Giardino JR, Vitek JD (2010b) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115: 172–187. DOI: 10.1016/j.geomorph.2009.10.002Google Scholar
  87. Roberts A (2001) Curvature attributes and their interpretation to 3D interpreted horizons: First Break 19: 85–100.Google Scholar
  88. Shahabi H, Khezri S, Ahmad BB, et al. (2014) Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 115: 55–70. DOI: 10.1016/j.catena.2013.11.014Google Scholar
  89. Santacana N, Baeza B, Corominas J, et al. (2003) AGIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain). Natural Hazards 30: 281–295. DOI: 10.1023/B:NHAZ.0000007169.28860.80Google Scholar
  90. Searle MP, Windley B, Coward M, et al. (1987) The closing of Tethys and the tectonics of the Himalaya. Geological Society of America Bulletin 98: 678–701. DOI: 10.1130/0016-7606(1987)98<678:TCOTAT>2.0.CO;2Google Scholar
  91. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications 38(7): 8208–8219. DOI: 10.1016/j.eswa.2010.12.167Google Scholar
  92. Sharma M, Kumar R (2008) GIS-based landslide hazard zonation: a case study from the Parwanoo area, Lesser and Outer Himalaya, H.P., India. Bulletin of Engineering Geology and the Environment 67: 129–137. DOI: 10.1007/s10064-007-0113-2Google Scholar
  93. Sidle RC, Pearce AJ, O’Loughlin CL (1985) Hillslope Stability and Land Use (Water Resources Monograph). American Geophys. Union, Washington, D.C., USA.Google Scholar
  94. Soeters R, van Westen CJ (1996) Slope stability recognition analysis and zonation. In: Turner AK, Schuster RL (Eds.) Landslides: Investigation and Mitigation, Transportation Research Board Special Report 247. National Academy Press, Washington, D.C., USA. pp 129–177.Google Scholar
  95. Tamrakar NK and Yokota S (2008) Types and processes of slope movements along East-West Highway, Surai Khola area, Mid-Western Nepal Sub-Himalaya. Bulletin of the Department of Geology, Tribhuvan University, Kathmandu, Nepal. 11: 1–4.Google Scholar
  96. Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118: 124–135. DOI: 10.1016/j.catena.2014.02.005Google Scholar
  97. Vahidnia MH, Alesheikh AA, Alimohammadi A, et al. (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Computers & Geosciences 36(29): 1101–1114. DOI: 10.1016/j.cageo.2010.04.004Google Scholar
  98. Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards 30: 399–419. DOI: 1023/B:NHAZ.0000007097.42735.9eGoogle Scholar
  99. Varnes DJ, International Association of Engineering Geology Commission on Landslides and Other Mass Movement on Slopes (1984) Landslides Hazard Zonation: A Review of Principles and Practice. Natural Hazards, Vol. 3. Published by UNESCO, Paris, FranceGoogle Scholar
  100. Wu CH, Chen SC (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112: 190–204. DOI: 10.1016/j.geomorph.2009.06.002Google Scholar
  101. Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GISbased 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): 274–287. DOI: 10.1016/j.catena.2011.01.014Google Scholar
  102. 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(4): 572–582. DOI: 10.1016/j.geomorph.2008.02.011Google Scholar
  103. 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). Engineering Geology 79: 251–266. DOI: 10.1016/j.enggeo. 2005.02.002Google Scholar
  104. 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). Computers & Geosciences 35(6): 1125–1138. DOI: 10.1016/j.cageo.2008.08.007Google Scholar
  105. Yoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method. Landslides 3: 149–158. DOI: 10.1007/s10346-005-0031-yGoogle Scholar
  106. Zare M, Pourghasemi HR, Vafakhah M, et al. (2013) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences 6(8): 2873–2888. DOI: 10.1007/s12517-012-0610-xGoogle Scholar
  107. Zhu C, Wang X (2009) Landslide susceptibility mapping: a comparison of information and weights-of evidence methods in Three Gorges Area. International Conference on Environmental Science and Information Application Technology 3: 342–346. DOI: 10.1109/ESIAT.2009.187Google Scholar
  108. Zhu A, Wang R, Qiao J, et al. (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214: 128–138.Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Amar Deep Regmi
    • 1
    Email author
  • Kohki Yoshida
    • 2
  • Hamid Reza Pourghasemi
    • 3
  • Megh Raj DhitaL
    • 4
  • Biswajeet Pradhan
    • 5
  1. 1.Faculty of TechnologyNepal Academy of Science and TechnologyLalitpur-KhumaltarNepal
  2. 2.Department of Geology, Faculty of ScienceShinshu UniversityMatsumotoJapan
  3. 3.College of Natural Resources & Marine SciencesTarbiat Modares University (TMU)NoorTehran, Iran
  4. 4.Central Department of GeologyTribhuvan UniversityKritipurNepal
  5. 5.Faculty of Engineering, Department of Civil Engineering, Geosptial Information Science Research Centre (GISRC)University of Putra Malaysia, SerdangSelangor Darul EhsanMalaysia

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