Advertisement

Geomorphic Diversity and Landslide Susceptibility: A Multi-criteria Evaluation Approach

  • Sujit MandalEmail author
  • Subrata Mondal
Chapter
Part of the Environmental Science and Engineering book series (ESE)

Abstract

The present study attempts to assess the role of basin morphometric parameters in slope instability using morphometric diversity (MD) model. Also try to find out the role of drainage parameters and relief parameters in slope failure using drainage diversity (DD) and relief diversity (RD) models respectively. For that total 14 morphometric data layers were considered. The relationship of each data layers with landslide susceptibility was judge using frequency ratio (FR) approach. Parameters like drainage density, drainage frequency, relative relief, drainage texture, junction frequency, infiltration number, ruggedness index, dissection index, elevation, slope, relief ratio and hypsometric integral were positively related with landslide potentiality while bifurcation ratio and drainage intensity negatively correlated with slope failure. The principal component analysis (PCA) based weight assign to each data layers of each model which multiplied with unidirectional reclassified data layers for each model using weighted linear combination (WLC) approach to prepare landslide susceptibility maps. The receiver operating characteristics curve showed that, the landslides prediction accuracy of the DD, RD and MD models was 71.4, 73.9 and 76.3% respectively. The FR plots of the aforesaid three models suggested that, the chance of landslide increases from very low to very high susceptibility zones.

Keywords

Relief diversity (RD) Drainage diversity (DD) Morphometric diversity (MD) Weighted linear combination approach Landslide susceptibility Validation 

References

  1. Akgun A, Sezer EA, Nefesliogl HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34CrossRefGoogle Scholar
  2. Althuwaynee OF, Pradhan B, Park H, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36CrossRefGoogle Scholar
  3. Amani M, Safaviyan A (2015) Sub-basins prioritization using morphometric analysis- remote sensing technique and GIS-Golestan-Iran. Int Lett Nat Sci 38:56–65Google Scholar
  4. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277CrossRefGoogle Scholar
  5. Ardizzone F, Cardinali M, Carrara A, Guzzetti F, Reichenbach P (2002) Impact of mapping errors on the reliability of landslide hazard maps. Nat Hazards Earth Syst Sci 2:3–14CrossRefGoogle Scholar
  6. Avinash K, Deepika B, Jayappa KS (2014) Basin geomorphology and drainage morphometry parameters used as indicators for groundwater prospect: insight from geographical information system (GIS) technique. J Earth Sci 25(6):1018–1032.  https://doi.org/10.1007/s12583-014-0505-8CrossRefGoogle Scholar
  7. 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 81:432–445CrossRefGoogle Scholar
  8. 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
  9. Chau KT, Sze YL, Fung MK, Wong WY, Fong EL, Chan LCP (2004) Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput Geosci 30:429–443CrossRefGoogle Scholar
  10. Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2011) 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
  11. Chung C-JF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogram Eng Remote Sens 65(12):1389–1399Google Scholar
  12. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS Lantau Island Hong Kong. Geomorphology 42:213–228CrossRefGoogle Scholar
  13. Devkota KC, Regmi AD, Pourghasemi HR et al (2013a) 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(1):135–165.  https://doi.org/10.1007/s11069-012-0347-6CrossRefGoogle Scholar
  14. Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013b) 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 65:135–165CrossRefGoogle Scholar
  15. Dove N (1957) The ratio of relative and absolute altitude of Mt.Camel. Geog Rev 47:564–569CrossRefGoogle Scholar
  16. Faniran A (1968) The index of drainage intensity—a provisional new drainage factor. Aust J Sci 31:328–330Google Scholar
  17. Farhan Y, Anbar A, Enaba O, Al-Shaikh N (2015) Quantitative analysis of geomorphometric parameters of Wadi Kerak, Jordan, using remote sensing and GIS. J Water Resour Protect 7:456–475.  https://doi.org/10.4236/jwarp.2015.76037CrossRefGoogle Scholar
  18. Foumelis M, Lekkas E, Parcharidis I (2004) Landslide susceptibility mapping by GIS-based qualitative weighting procedure in Corinth area. Bull Geol Soc Greece XXXVIGoogle Scholar
  19. Gajbhiye S, Mishra SK, Pandey A (2014) Prioritizing erosion-prone area through morphometric analysis: an RS and GIS perspective. Appl Water Sci 4(1):51–61CrossRefGoogle Scholar
  20. Ghosh D (2015) Landslide susceptibility analysis from morphometric parameter analysis of Riyong Khola basin, West Sikkim, India: a geospatial approach. Int J Geol 5(1):54–65Google Scholar
  21. Ghosh KG, Saha S (2015) Identification of soil erosion susceptible areas in Hinglo river basin, Eastern India based on geo-statistics. Univers J Environ Res Technol 5(3)Google Scholar
  22. Ghosh S, Carranza EJM, van Westen CJ, Jetten VG, Bhattacharya DN (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131(1–2):35–56.  https://doi.org/10.1016/j.geomorph.2011.04.019CrossRefGoogle Scholar
  23. Gopal KG, Saha S (2015) Identification of soil erosion susceptible areas in Hinglo river basin, Eastern India based on geostatistics. Univ J Environ Res Technol 5(3):152–164Google Scholar
  24. Gupta RP, Joshi BC (1990) Landslide Hazard Zonation using the GIS Approach—a case Study from the Ramganga Catchment Himalayas. Eng Geol 28:119–131CrossRefGoogle Scholar
  25. Horton RE (1932) Drainage basin characteristics. Am Geophys Union 13:350–361CrossRefGoogle Scholar
  26. Horton RE (1945) Erosional development of streams and their drainage basins, a hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56:275–370CrossRefGoogle Scholar
  27. Jeganathan C, Chauniyal DD (2000) An evidential weighted approach for landslide hazard zonation from geo-environmental characterization: a case study of Kelani area. Curr Sci 79(2):238–243Google Scholar
  28. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility Zonation in Darjeeling Himalayas. Eng Geol 85:347–366CrossRefGoogle Scholar
  29. Karim S, Jalileddin S, Ali MT (2011) Zoning landslide by use of frequency ratio method (case study: Deylaman Region). Middle East J Sci Res 9(5):578–583Google Scholar
  30. Khatun S, Pal S (2017) Categorization of morphometric surface through morphometric diversity analysis in Kushkarani River basin of Eastern India. Asian J Phys Chem Sci 2(1):1–19.  https://doi.org/10.9734/ajopacs/2017/31098CrossRefGoogle Scholar
  31. Kienholz H (1978) Maps of geomorphology and natural hazards of Grindelwald, Switzerland: scale 1:10,000. Arct Alp Res 10:169–184CrossRefGoogle Scholar
  32. Kumar K, Garbyal Y (2016) Analysis of morphometric parameters for the identification of probable landslide occurrences. In: Conference: Geo-Chicago, 14–16 Aug 2016, ASCE library, subject heading- Himalayas, Chicago, Illinois At.  https://doi.org/10.1061/9780784480120.035
  33. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41CrossRefGoogle 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 50:847–855CrossRefGoogle Scholar
  35. Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990CrossRefGoogle Scholar
  36. Magesh NS, Jitheshlal K, Chandrasekar N, Jini K (2012) GIS based morphometric evaluation of Chimini and Mupily watersheds, parts of Western Ghats, Thrissur district, Kerala, India. Earth Sci Inform 5:111–121CrossRefGoogle Scholar
  37. Mahalingam R, Olsen MJ, O’Banion MS (2016) Evaluation of landslide susceptibility mapping techniques using lidar–derived conditioning factors (Oregon case study). Geomat Nat Hazard Risk 7:1884–1907CrossRefGoogle Scholar
  38. Majtan S, Omura H, Morita K (2002) Fractal dimension as an indicator of probability for landslides in North Matsuura Japan. Geograficky Casopis 54:5–19Google Scholar
  39. Mandal B, Mandal S (2016) Assessment of mountain slope instability in the Lish river basin of Eastern Darjeeling Himalaya using frequency ratio model (FRM). Earth Syst Environ 2:121.  https://doi.org/10.1007/s40808-016-0169-8CrossRefGoogle Scholar
  40. Mandal S, Mandal K (2017) Bivariate statistical index for landslide susceptibility mapping in the Rorachu River basin of Eastern Sikkim Himalaya, India. Spat Inf Res.  https://doi.org/10.1007/s41324-017-0156-9CrossRefGoogle Scholar
  41. Mandal S, Mandal K (2018) Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Model Earth Syst Environ.  https://doi.org/10.1007/s40808-018-0426-0CrossRefGoogle Scholar
  42. Miller CT, Poirier-McNeill MM, Mayer AS (1990) Dissolution of trapped nonaqueous phase liquids: mass transfer characteristics. Water Resour Res 26:2783–2796CrossRefGoogle Scholar
  43. 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 61:221–236CrossRefGoogle Scholar
  44. Mondal S, Mandal S (2017a) RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model. Georisk Assess Manag Risk Eng Syst Geohazards.  https://doi.org/10.1080/17499518.2017.1347949Google Scholar
  45. Mondal S, Mandal S (2017b) Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya. Spat Inf Res.  https://doi.org/10.1007/s41324-017-0101-yCrossRefGoogle Scholar
  46. Nag SK (1998) Morphometric analysis using remote sensing techniques in the Chaka sub-basin, Purulia district, West Bengal. J Indian Soc Remote Sens 26(1):69–76CrossRefGoogle Scholar
  47. Nautiyal MD (1994) Morphometric analysis of drainage basin, district Dehradun, Uttar Pradesh. J Indian Soc Remote Sens 22(1994):252–262Google Scholar
  48. Nefeslioglu HA, Sezer E, Go¨kc¸eog˘lu C, Bozkır AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul Turkey. Math Prob Eng (Article ID: 901095)CrossRefGoogle Scholar
  49. Nidhi K, Chowdary VM, Tiwari KN, Shinde V, Dadhwal VK (2016) Assessment of surface water potential using morphometry and curve number-based approaches. Geocarto Int 32(11):1206–1228.  https://doi.org/10.1080/10106049.2016.1195889CrossRefGoogle Scholar
  50. 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 64:180–197CrossRefGoogle Scholar
  51. Poli S, Sterlacchini S (2007) Landslide representation strategies in susceptibility studies using weights-of-evidence modeling technique. Nat Res Res 16(2).  https://doi.org/10.1007/s11053-007-9043-8CrossRefGoogle Scholar
  52. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci.  https://doi.org/10.1007/s12517-012-0532-7CrossRefGoogle Scholar
  53. Pradhan B (2010) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349CrossRefGoogle Scholar
  54. Pradhan B, Lee S (2010a) 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
  55. Pradhan B, Lee S (2010b) 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):747–759CrossRefGoogle Scholar
  56. Rai PK, Mishra S, Ahmad A, Mohan K (2014) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci.  https://doi.org/10.1007/s13201-014-0238-yCrossRefGoogle Scholar
  57. Rastogi RA, Sharma TC (1976) Quantitative analysis of drainage basin characteristics. J Soil Water Conserv India 26(1&4):18–25Google Scholar
  58. Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogram Eng Remote Sens 70(5):617–625CrossRefGoogle Scholar
  59. Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Bull Geol Soc Am 67:597CrossRefGoogle Scholar
  60. Shrestha S, Kang T, Suwal MS (2017) An ensemble model for co-seismic landslide susceptibility using GIS and random forest method. Int J Geo-Inf 6:365.  https://doi.org/10.3390/ijgi6110365CrossRefGoogle Scholar
  61. Smith GH (1935) The relative relief of Ohio. Geogr Rev India 25:272–284CrossRefGoogle Scholar
  62. Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake induced landslides using Bayesian network: a case study in Beichuan China. Comput Geosci 42:189–199CrossRefGoogle Scholar
  63. Spiker EC, Gori PL (2000) National landslide hazards mitigation strategy: a framework for loss reduction. Department of the interior, U.S. Geol Surv 59Google Scholar
  64. Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Geol Soc Am Bull 63:117–142Google Scholar
  65. Strahler AN (1964) Quantitative geomorphology of drainage basins and channel networks. In: Chow VT (ed) Handbook of applied hydrology, pp 439–476Google Scholar
  66. Thakkar AK, Dhiman SD (2007) Morphometric analysis and prioritization of mini watershed s in Mohr watersheds. Gujarat using remote sensing and GIS techniques. J Indian Soc Remote Sens 33(1):25–38Google Scholar
  67. Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Native Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 145:164–179CrossRefGoogle Scholar
  68. Umar Z, Pradhan B, Ahmad A et al (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.  https://doi.org/10.1016/j.catena.2014.02.005CrossRefGoogle Scholar
  69. Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed China. Geomorphology 145–146:70–80CrossRefGoogle Scholar
  70. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey; Ph.D thesis, Department of Geomatics, University of Melbourne, 423 pGoogle Scholar
  71. Yin KJ, Yin TZ (1988) Statistical prediction model for slope instability of metamorphosed rock. In: Proceedings of 5th international symposium on landslides Lausanne, Switzerland 2, pp 1269–1272Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of GeographyDiamond Harbour Women’s UniversityDiamond HarbourIndia
  2. 2.Bajitpur High SchoolGangarampurIndia

Personalised recommendations