Abstract
Among the various natural hazards, slope failure is the most widespread and damaging hazard (De Smedt in Slope stability analysis using GIS on a regional scale: a case study of Narayanghat Mungling highway section, Nepal, 2005). A sudden failure of the slope is caused by sliding, rolling, falling or slumping. When failure occurs, material is transported down slope until a stable slope condition is re-established. The Darjeeling Himalayan terrain is very high susceptible to slope instability due to a complex geological structure and complex interaction among various processes acting upon the steep mountain southern escarpment slopes. In Darjeeling, the spatial extents of landslides are increasing day by day and causing severe damage to lives and properties. The Balason river basin is not an exception to it.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 96:28–40
Chen W, Li W (2014) Application of weights-of-evidence model in landslide susceptibility mapping at Baozhong Region in Baoji, China. Electron J Geotech Eng 19
Chen W, Li W, Hou E (2014) Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China. Arab J Geosci 7:4499–4511
Choi J, Oh H-J, Lee H-J, 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 124:12–23. https://doi.org/10.1016/j.enggeo.2011.09.011
Devkota KC, Regmi AD, Yoshida KH, Pourghasemi R, Pradhan B, Althuwaynee OF, Dhital MR (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. Nat Hazards 65:135–165. https://doi.org/10.1007/s11069-012-0347-6
De Smedt F (2005) Slope stability analysis using GIS on a regional scale: a case study of Narayanghat Mungling highway section, Nepal. Dissertation of Master Degree, Universiteit Gent, Belgium
Dou J, Yamagishi H, Pourghasemi HR, Song X, Xu Y, Zhu Z (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776. https://doi.org/10.1007/s11069-015-1799-2
Gupta RP, Joshi BC (1990) Landslide hazard zonation using the GIS approach—a case study from the Ramganga Catchment, Himalayas. Eng Geol 28:119–131
Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281
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 61:221–236
Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow land sliding. Water Resour Res 30(4):1153–1171
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. J Asian Earth Sci 64:180–197
Pradhan B (2013) 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 51:350–365
Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759
Pradhan B, Oh HJ, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1(3):199–223
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):2351–2365
Sharma LP, Patel N, Ghose MK, Debnath P (2013) Synergistic application of fuzzy logic and geoinformatics for landslide vulnerability zonation—a case study in Sikkim Himalayas, India. Appl Geomat 5:271–284
Sujatha ER, Rajamanickam GV, Kumaravel P (2012) Landslide susceptibility analysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed, India. J Earth Syst Sci 121(5):1337–1350
Torkashvand AM, Irani A, Sorur J (2014) The preparation of landslide map by Landslide Numerical Risk Factor (LNRF) model and Geographic Information System (GIS). Egypt J Remote Sens Space Sci 17:159–170
van Westen CJ (1997) Statistical landslide hazard analysis. In: Application guide, ILWIS 2.1 for Windows. ITC, Enschede, The Netherlands, pp 73–84
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):274–287
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Mandal, S., Mondal, S. (2019). Comparison Between Landslide Susceptibility Models: A Critical Review and Evaluation. In: Geoinformatics and Modelling of Landslide Susceptibility and Risk. Environmental Science and Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-10495-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-10495-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10494-8
Online ISBN: 978-3-030-10495-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)