Skip to main content
SpringerLink
Log in

Assessment of Slope Instability in a Hilly Terrain: A Logistic Regression and Random Forest Based Approach

  • Conference paper
  • First Online:
Computational Intelligence in Communications and Business Analytics (CICBA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1955))

  • 88 Accesses

Abstract

Landslides are one such spontaneous natural disaster which has the potential to effect human lives as well as economic property in any region. The continuous exploration of qualitative and quantitative approaches augmented with RS-GIS methodologies for assessing landslides have given new dimensions to the area of research. The main obstacle of these methodologies remains in the fact that the hypothesis needs to be taken as true even before the analysis. To overcome the same, with the development of machine learning methodologies, the exhibits are found to be more objective in terms of quantification as well as the quality of research. The current assessment was aimed to evaluate the execution of two machine learning approaches namely; logistic regression and random forest for the assessment of slope instability in Darjeeling Himalayas. Eight geo-environmental factors were taken into consideration for the said assessment. The susceptibility models prepared with the said approaches were substantiated using receiver operating characteristics curves. The assessed accuracies were 76.8% and 79.7% for logistic regression and random forest respectively with the dataset(training and validation), the accuracies measured were 76.8% and 77.7% respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gerrard, J.: The landslide hazard in the Himalayas: geological control and human action. Geomorphology 10, 221–230 (1994). https://doi.org/10.1016/0169-555x(94)90018-3

    Article  Google Scholar 

  2. Solaimani, K., Mousavi, S.Z., Kavian, A.: Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab. J. Geosci. 6, 2557–2569 (2012). https://doi.org/10.1007/s12517-012-0526-5

    Article  Google Scholar 

  3. Tien Bui, D., 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, 361–378 (2015). https://doi.org/10.1007/s10346-015-0557-6

    Article  Google Scholar 

  4. Fan, X., et al.: Two multi-temporal datasets that track the enhanced landsliding after the 2008 Wenchuan earthquake. Earth System Science Data. 11, 35–55 (2019). https://doi.org/10.5194/essd-11-35-2019

    Article  Google Scholar 

  5. Chen, W., Pourghasemi, H.R., Naghibi, S.A.: A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bull. Eng. Geol. Env. 77, 647–664 (2017). https://doi.org/10.1007/s10064-017-1010-y

    Article  Google Scholar 

  6. Pradhan, S.P., Siddique, T.: Stability assessment of landslide-prone road cut rock slopes in Himalayan terrain: a finite element method based approach. J. Rock Mechanics and Geotechnical Engineering. 12, 59–73 (2020). https://doi.org/10.1016/j.jrmge.2018.12.018

    Article  Google Scholar 

  7. Kanwal, S., Atif, S., Shafiq, M.: GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomat. Nat. Haz. Risk 8, 348–366 (2016). https://doi.org/10.1080/19475705.2016.1220023

    Article  Google Scholar 

  8. Chen, W., Ding, X., Zhao, R., Shi, S.: Application of frequency ratio and weights of evidence models in landslide susceptibility mapping for the Shangzhou District of Shangluo City. China. Environmental Earth Sciences 75, (2015). https://doi.org/10.1007/s12665-015-4829-1

  9. Juliev, M., Mergili, M., Mondal, I., Nurtaev, B., Pulatov, A., Hübl, J.: Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District. Uzbekistan. Science of The Total Environment. 653, 801–814 (2019). https://doi.org/10.1016/j.scitotenv.2018.10.431

    Article  Google Scholar 

  10. Chen, W., et al.: Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. CATENA 172, 212–231 (2019). https://doi.org/10.1016/j.catena.2018.08.025

    Article  Google Scholar 

  11. Youssef, A.M., Pourghasemi, H.R.: Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region. Saudi Arabia. Geoscience Frontiers 12, 639–655 (2021). https://doi.org/10.1016/j.gsf.2020.05.010

    Article  Google Scholar 

  12. Ado, M., et al.: Landslide susceptibility mapping using machine learning: a literature survey. Remote Sensing 14, 3029 (2022). https://doi.org/10.3390/rs14133029

    Article  Google Scholar 

  13. Mondal, S., Mandal, S.: Geomorphic diversity and landslide susceptibility in the Balason River Basin. Darjeeling Himalaya. HKIE Transactions 27, 13–24 (2020). https://doi.org/10.33430/v27n1thie2017-0054

  14. Chawla, A., Pasupuleti, S., Chawla, S., Rao, A.C.S., Sarkar, K., Dwivedi, R.: Landslide susceptibility zonation mapping: a case study from Darjeeling District, Eastern Himalayas, India. J. Indian Society of Remote Sensing 47, 497–511 (2019). https://doi.org/10.1007/s12524-018-0916-6

    Article  Google Scholar 

  15. Roy, J., Saha, S.: Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenvironmental Disasters 6 (2019). https://doi.org/10.1186/s40677-019-0126-8

  16. Hemasinghe, H., Rangali, R.S.S., Deshapriya, N.L., Samarakoon, L.: Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka). Procedia Engineering 212, 1046–1053 (2018). https://doi.org/10.1016/j.proeng.2018.01.135

    Article  Google Scholar 

  17. Rasyid, A.R., Bhandary, N.P., Yatabe, R.: Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters 3 (2016). https://doi.org/10.1186/s40677-016-0053-x

  18. Gayen, A., Saha, S.: Deforestation probable area predicted by logistic regression in Pathro river basin: a tributary of Ajay river. Spat. Inf. Res. 26, 1–9 (2017). https://doi.org/10.1007/s41324-017-0151-1

    Article  Google Scholar 

  19. Breiman: Random Forests. Machine Learning 45, 5–32. https://doi.org/10.1023/A:1010933404324

  20. Stumpf, A., Kerle, N.: Object-oriented mapping of landslides using random forests. Remote Sens. Environ. 115, 2564–2577 (2011). https://doi.org/10.1016/j.rse.2011.05.013

    Article  Google Scholar 

  21. Taalab, K., Cheng, T., Zhang, Y.: Mapping landslide susceptibility and types using Random Forest. Big Earth Data. 2, 159–178 (2018). https://doi.org/10.1080/20964471.2018.1472392

    Article  Google Scholar 

  22. Oh, H.-J., Lee, S., Hong, S.-M.: Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. J. Sensors 2017, 1–21 (2017). https://doi.org/10.1155/2017/3730913

    Article  Google Scholar 

  23. Samui, P.: Slope stability analysis: a support vector machine approach. Environ. Geol. 56, 255–267 (2008). https://doi.org/10.1007/s00254-007-1161-4

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, University of North Bengal, Darjeeling, West Bengal, India

    Sumon Dey & Swarup Das

Authors
  1. Sumon Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swarup Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sumon Dey .

Editor information

Editors and Affiliations

  1. Kalyani Government Engineering College, Kalyani, India

    Kousik Dasgupta

  2. Assam University, Silchar, India

    Somnath Mukhopadhyay

  3. University of Kalyani, Kalyani, West Bengal, India

    Jyotsna K. Mandal

  4. Visvabharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dey, S., Das, S. (2024). Assessment of Slope Instability in a Hilly Terrain: A Logistic Regression and Random Forest Based Approach. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48876-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48875-7

  • Online ISBN: 978-3-031-48876-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation