Skip to main content
SpringerLink
Log in

Landslide Susceptibility Using Weighted Regression Model: A Geo-spatial Approach

  • Chapter
  • First Online:
Landslide: Susceptibility, Risk Assessment and Sustainability

Part of the book series: Advances in Natural and Technological Hazards Research ((NTHR,volume 52))

  • 38 Accesses

Abstract

Steep geographical terrain is very vulnerable to the trend of landslip all around the world. Landslides take place regularly and on a yearly basis throughout India’s diverse hill and mountain ranges. Tamil Nadu’s Nilgiris district is especially susceptible to landslides because of the region’s abundant rainfall for the duration of the South and North East monsoons. The focus of this study is to define high-risk areas and pinpoint features that make landslides vulnerable. It is essential to use the landslide hazard zonation maps appropriately and conduct a thorough analysis of each slope that is vulnerable to landslides. Development, the risk of landslides today, and projected future slopes where we can place the early warning system for slope failure based on past landslide locations should all be represented on planning-level maps. Using the weighted regression model, a straightforward statistical method has been used to calculate the proximity of their relationship. Additionally, weighted regression models were useful in validating the chosen causal factors based on their capacity to prevent a landslide episode because they could explain in detail the variance in scores between the causative factors for each class of landslides as well as the distribution of landslides using geographic information systems (GIS). The outcome indicates a relationship between the likelihood of a landslip occurring and the slope, with steeper slopes having higher landslip probability and a slope prediction rate of 23.82. The research area’s western and eastern halves are heavily populated with high-susceptibility areas.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

  • Alexakis DD, Agapiou A, Tzouvaras M, Themistocleous K, Neocleous K, Michaelides S, Hadjimitsis DG (2014) Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus. Nat Hazards 72:119–141

    Article  Google Scholar 

  • Anis Z, Wissem G, Riheb H, Biswajeet P, Essghaier GM (2019) Effects of clay properties in the landslides genesis in flysch massif: case study of Aïn Draham, North Western Tunisia. J Afr Earth Sci 151:146–152

    Article  Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K, Conoscenti C (2019) Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. CATENA 180:282–297

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81

    Article  Google Scholar 

  • Bhagyasree Dolui YR, Geetha G (2020) Statistical analysis on the interrelationship between socio-demographic and anxiety of landslide among the tribal people

    Google Scholar 

  • Chen W, Chen Y, Tsangaratos P, Ilia I, Wang X (2020) Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens 12(23):3854

    Article  Google Scholar 

  • Conforti M, Ietto F (2020) Influence of tectonics and morphometric features on the landslide distribution: a case study from the Mesima Basin (Calabria, South Italy). J Earth Sci 31(2):393–409

    Article  Google Scholar 

  • Dolui B, Yuvaraj RM, Geetha G (2019) Landslide susceptibility mapping using AHP model in Nilgiri District. Themat J Geogr 8(12):189–208

    Google Scholar 

  • Dolui B, Yuvaraj RM, Geetha G (2023) Risk perception of landslide among the tribal population: a case study of Kotagiri Taluk of Nilgiri District, Tamil Nadu, India. Indigenous knowledge and disaster risk reduction: insight towards perception, response, adaptation and sustainability. Springer International Publishing, Cham, pp 209–225

    Chapter  Google Scholar 

  • Düzgün HS, Kemeç S (2008) Spatial and geographically weighted regression

    Google Scholar 

  • Farooq K, Rogers JD, Ahmed MF (2015) Effect of densification on the shear strength of landslide material: a case study from Salt Range, Pakistan. Earth Sci Res 4(1):113

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128

    Article  Google Scholar 

  • Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards 18(8):2161–2181

    Article  Google Scholar 

  • Ganapathy GP, Rajawat AS (2015) Use of hazard and vulnerability maps for landslide planning scenarios: a case study of the Nilgiris, India. Nat Hazards 77:305–316

    Article  Google Scholar 

  • Gomathi R, Mohan RV, Backiaraj S (2013) Landslide susceptibility zonation in Kukalthurai Halla watershed, Moyar sub-basin in Nilgiris mountains, South India using Remote Sensing and GIS. Int J Geomat Geosci 4(2):366

    Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216

    Article  Google Scholar 

  • Haque U, Blum P, Da Silva PF, Andersen P, Pilz J, Chalov SR et al (2016) Fatal landslides in Europe. Landslides 13:1545–1554

    Article  Google Scholar 

  • Hong H, Pradhan B, Sameen MI, Chen W, Xu C (2017) Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China). Geomat Nat Haz Risk 8(2):1997–2022

    Article  Google Scholar 

  • Huang F, Ye Z, Jiang SH, Huang J, Chang Z, Chen J (2021) Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models. CATENA 202:105250

    Article  Google Scholar 

  • Jaiswal P, Van Westen CJ (2009) Probabilistic landslide initiation hazard assessment along a transportation corridor in the Nilgiri area, India. In: EGU general assembly conference abstracts, p 2854

    Google Scholar 

  • Kanwal S, Atif S, Shafiq M (2017) GIS-based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomat Nat Haz Risk 8(2):348–366

    Article  Google Scholar 

  • Karim Z, Hadji R, Hamed Y (2019) GIS-based approaches for the landslide susceptibility prediction in Setif Region (NE Algeria). Geotech Geol Eng 37(1):359–374

    Article  Google Scholar 

  • Kirschbaum DB, Adler R, Hong Y, Hill S, Lerner-Lam A (2010) A global landslide catalog for hazard applications: method, results, and limitations. Nat Hazards 52:561–575

    Article  Google Scholar 

  • Kouli M, Loupasakis C, Soupios P, Vallianatos F (2010) Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece. Nat Hazards 52:599–621

    Article  Google Scholar 

  • Li Y, Liu X, Han Z, Dou J (2020) Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: a case study of Qingchuan area, China. Appl Sci 10(3):1107

    Article  Google Scholar 

  • Manchar N, Benabbas C, Hadji R, Bouaicha F, Grecu F (2018) Landslide susceptibility assessment in Constantine region (NE Algeria) by means of statistical models. Studia Geotechnica et Mechanica 40(3):208–219

    Google Scholar 

  • Marjanović M (2013) Advanced methods for landslide assessment using GIS. Doctoral dissertation, Palacký University Olomouc

    Google Scholar 

  • Neaupane KM, Piantanakulchai M (2006) Analytic network process model for landslide hazard zonation. Eng Geol 85(3–4):281–294

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Paez A (2006) Exploring contextual variations in land use and transport analysis using a probit model with geographical weights. J Transp Geogr 14(3):167–176

    Article  Google Scholar 

  • Papoutsis I, Kontoes C, Alatza S, Apostolakis A, Loupasakis C (2020) InSAR Greece with parallelized persistent scatterer interferometry: a national ground motion service for big Copernicus sentinel-1 data. Remote Sens 12(19):3207

    Article  Google Scholar 

  • Petley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930

    Article  Google Scholar 

  • Rajarathnam S, Ganapathy GP (2006) Landslide hazard zonation of India, a GIS approach. In: Proceedings of the first India disaster management congress, New Delhi, vol 29, p 30

    Google Scholar 

  • Shah SHIA, Jianguo Y, Jahangir Z, Tariq A, Aslam B (2022) Integrated geophysical technique for groundwater salinity delineation, an approach to agriculture sustainability for Nankana Sahib Area, Pakistan. Geomat Nat Haz Risk 13(1):1043–1064

    Article  Google Scholar 

  • Tariq A, Yan J, Gagnon AS, Riaz Khan M, Mumtaz F (2023) Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spat Inf Sci 26(3):302–320

    Article  Google Scholar 

  • Tsironi V, Ganas A, Karamitros I, Efstathiou E, Koukouvelas I, Sokos E (2022) Kinematics of active landslides in Achaia (Peloponnese, Greece) through InSAR time series analysis and relation to rainfall patterns. Remote Sens 14(4):844

    Article  Google Scholar 

  • Tu J (2011) Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Appl Geogr 31(1):376–392

    Article  Google Scholar 

  • Wheeler DC, Páez A (2009) Geographically weighted regression. In: Handbook of applied spatial analysis: software tools, methods and applications. Springer, Berlin, Heidelberg, pp 461–486

    Google Scholar 

  • Yuvaraj RM, Dolui B (2021) Statistical and machine intelligence-based model for landslide susceptibility mapping of Nilgiri district in India. Environ Chall 5:100211

    Article  Google Scholar 

  • Yuvaraj RM, Dolui B (2023) Geographical assessment of landslide susceptibility using statistical approach. Quat Sci Adv 11:100097

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Madras, Chennai, Tamil Nadu, India

    R. M. Yuvaraj

  2. Department of Geography, Queen Mary’s College, Chennai, Tamil Nadu, India

    Bhagyasree Dolui

Authors
  1. R. M. Yuvaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhagyasree Dolui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. M. Yuvaraj .

Editor information

Editors and Affiliations

  1. Department of Geography, KISS Deemed University, School of Tribal Resource Development, Bhubaneswar, India

    Gopal Krishna Panda

  2. Graduate School of Media and Governance, Keio University Shonan Fujisawa, Japan, Japan

    Rajib Shaw

  3. Department of Geography, The University of Burdwan, Bardhaman, India

    Subodh Chandra Pal

  4. Department of Geography, Bhatter College, Dantan (Vidyasagar Univ, Kharagpur, Paschim Medinipore,, India

    Uday Chatterjee

  5. Department of Geography, The University of Burdwan, Bardhaman, India

    Asish Saha

Rights and permissions

Reprints and permissions

Copyright information

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

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yuvaraj, R.M., Dolui, B. (2024). Landslide Susceptibility Using Weighted Regression Model: A Geo-spatial Approach. In: Panda, G.K., Shaw, R., Pal, S.C., Chatterjee, U., Saha, A. (eds) Landslide: Susceptibility, Risk Assessment and Sustainability. Advances in Natural and Technological Hazards Research, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-031-56591-5_12

Download citation

Publish with us

Policies and ethics

Search

Navigation