Advertisement

Landslide Susceptibility Zonation (LSZ) Using Machine Learning Approach for DEM Derived Continuous Dataset

  • Muskan Jhunjhunwalla
  • Sharad Kumar Gupta
  • Dericks P. ShuklaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

Landslide-prone areas can be shown by depicting occurrence of landslides; landslide susceptibility zonation map (LSZ), landslide hazard zonation map (LHZ) and landslide risk zonation map (LRZ). However, for the preparation of LRZ map, we need LHZ map and for LHZ map, we need LSZ map. In this work Logistic Regression (LR), Fisher Discriminant Analysis (FDA) associated with the weighted linear combination (WLC) and ANN are used for the preparation of LSZ maps. Seven causative factors that give continuous dataset are taken into consideration i.e. aspect, slope, digital elevation model (DEM), topographic wetness index (TWI), tangential curvature, profile curvature and plan curvature for the part of Mandakini river basin in Garhwal Himalayas. Geology, geomorphology, soil type, thrust/fault buffer, road buffer, drainage buffer etc., which are also important landslide governing factors, were not used as they give a discrete/classified dataset. The study area is spread in 275.60 km\(^2\) area where total 122 landslides occurred between 2004 and 2017. The landslides occurred from 2004 to 2012 (46 landslides with 1203 pixels) have been used for training of the models and from 2013 to 2017 (76 landslides) have been used for testing of the models. The susceptibility maps were classified/categorized into five different zones (very low, low, moderate, high and very high) based on the natural break in data. The landslide locations with the index value greater than 0.55 have been considered for the validation of the maps. The assessment of accuracy is done based on the Heidke Skill Score (HSS). The HSS score for FDA, LR and ANN is obtained as 0.89, 0.98 and 0.96. Based on the HSS score, the LR method can be selected as the best method amongst the three.

Keywords

Landslide susceptibility zonation (LSZ) Artificial neural network Fisher discriminant analysis Logistic regression Weighted linear combination Heidke skill score 

References

  1. 1.
    Gupta, S.K., Shukla, D.P., Thakur, M.: Selection of weightages for causative factors used in preparation of landslide susceptibility zonation (lsz). Geomatics Nat. Hazards Risk 9(1), 471–487 (2018)CrossRefGoogle Scholar
  2. 2.
    Zare, M., Pourghasemi, H.R., Vafakhah, M., Pradhan, B.: 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. Arab. J. Geosci. 6(8), 2873–2888 (2013)CrossRefGoogle Scholar
  3. 3.
    Singh, A.K.: Causes of slope instability in the Himalayas. Disaster Prev. Manag. Int. J. 18(3), 283–298 (2009)CrossRefGoogle Scholar
  4. 4.
    Pradhan, B., Abokharima, M.H., Jebur, M.N., Tehrany, M.S.: Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat. Hazards 73(2), 1019–1042 (2014)CrossRefGoogle Scholar
  5. 5.
    Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Mohammadi, M., Moradi, H.R.: 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 (2013)CrossRefGoogle Scholar
  6. 6.
    Lee, S., Pradhan, B.: Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1), 33–41 (2007)CrossRefGoogle Scholar
  7. 7.
    Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A.: Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3–4), 379–400 (2008)CrossRefGoogle Scholar
  8. 8.
    Nourani, V., Pradhan, B., Ghaffari, H., Sharifi, S.S.: Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat. Hazards 71, 523–547 (2013)CrossRefGoogle Scholar
  9. 9.
    Youssef, A.M., Pradhan, B., Jebur, M.N., El-Harbi, H.M.: Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area. Saudi Arabia. Environ. Earth Sci. 73(7), 3745–3761 (2015)CrossRefGoogle Scholar
  10. 10.
    Bui, D.T., Tuan, T.A., Klempe, H., Pradhan, B., Revhaug, I.: Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2), 361–378 (2015)Google Scholar
  11. 11.
    Kumar, D., Thakur, M., Dubey, C.S., Shukla, D.P.: Landslide susceptibility mapping and prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295, 115–125 (2017)CrossRefGoogle Scholar
  12. 12.
    Boroumandi, M., Khamehchiyan, M., Nikoudel, M.R.: Using of analytic hierarchy process for landslide hazard zonation in Zanjan province. Iran. Eng. Geol. Soc. Territ. 2, 951–955 (2015)Google Scholar
  13. 13.
    Pourghasemi, H.R., Pradhan, B., Gokceoglu, C.: Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed. Iran. Nat. Hazards 63(2), 965–996 (2012)CrossRefGoogle Scholar
  14. 14.
    Bouguelia, M.R., Nowaczyk, S., Santosh, K.C., Verikas, A.: Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int. J. Mach. Learn. Cybern. 9(8), 1307–1319 (2018)CrossRefGoogle Scholar
  15. 15.
    Stumpf, A., Lachiche, N., Malet, J.P., Kerle, N., Puissant, A.: Active learning in the spatial domain for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 52(5), 2492–2507 (2014)CrossRefGoogle Scholar
  16. 16.
    Stumpf, A., Lachiche, N., Kerle, N., Malet, J.P., Puissant, A.: Adaptive spatial sampling with active random forest for object-oriented landslide mapping. In: 2012 IEEE International Geoscience and Remote Sensing Symposium, pp. 87–90. IEEE (2012)Google Scholar
  17. 17.
    Li, C., Wang, B.: Fisher linear discriminant analysis (2014)Google Scholar
  18. 18.
    Bishop, C.M.: Fisher’s Linear Discriminant. Springer, New York (2006)Google Scholar
  19. 19.
    Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural network toolbox™ user’s guide. In: R2017a, The MathWorks Inc., 3 Apple Hill Drive Natick, MA 01760–2098. Citeseer (2017). http://www.mathworks.com
  20. 20.
    Michael, E.A., Samanta, S.: Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques. Model. Earth Syst. Environ. 2(2), 1–15 (2016)CrossRefGoogle Scholar
  21. 21.
    Malczewski, J.: On the use of weighted linear combination method in GIS: common and best practice approaches. Trans. GIS 4(1), 5–22 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Muskan Jhunjhunwalla
    • 1
  • Sharad Kumar Gupta
    • 2
  • Dericks P. Shukla
    • 2
    Email author
  1. 1.National Institute of Technology HamirpurHamirpurIndia
  2. 2.School of EngineeringIndian Institute of Technology MandiKamandIndia

Personalised recommendations