Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey)

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Turkey confronts loss of life and large economic losses due to natural disasters caused by its morphologic structure, geographical placement, and climate characteristics. The Kuzulu (Koyulhisar) landslide, which caused loss of life and property on 17th March 2005, occurred in an area near the country’s most important active fault, the North Anatolian Fault Zone. To mitigate and prevent landslide damages, prediction of landslide susceptibility areas based on probabilistic methods has a great importance. The purpose of this study was to produce a landslide susceptibility map by the logistic regression and frequency ratio methodologies for a 733-km2 area near the North Anatolian Fault Zone from the southeast of Niksar to Resadiye in Tokat province. Conditioning parameters, such as elevation, slope gradient, slope aspect, distance to streams, roads, and faults, drainage density, and fault density, were used in the analysis. Before susceptibility analysis, the landslides observed in the area were separated into two groups for use in analysis and verification, respectively. The susceptibility maps produced had five different susceptibility classes such as very low, low, moderate, high, and very high. To test the performance of the susceptibility maps, area under curve (AUC) approach was used. For the logistic regression method, the AUC value was 0.708; while for the frequency rate method, this value was 0.744. According to these AUC values, it could be concluded that the two landslide susceptibility maps obtained were successful.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Afifi AA, Clark V (1998) Computer aided multivariate analysis. Chapman Hall, London, 455p

    Google Scholar 

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multicriteria decision and likelihood ratio methods: case study at Izmir, Turkey. Landslides 9(1):93–106

    Article  Google Scholar 

  • Akgun A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51:1377–1387

    Article  Google Scholar 

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2011) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34

    Article  Google Scholar 

  • Akgun A, Kıncal C, Pradhan B (2012) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess 184(9):5453–5470

    Google Scholar 

  • ArcGIS (2009). Integrated GIS Software. ESRI, CA.

  • Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to attributes. Eng Geol 32:81–100

    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 

  • Bayazıt M (1999) Hydrology, Sayı:1605, 7. baskı, 127–131,Teknik Universitesi Matbaası, İstanbul

  • Bayazıt M (2001) Hydrology, 143–145, Birsen Yayınevi, İstanbul

  • Bijukchhen SM, Kayastha P, Dhital MR (2013) A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, east Nepal. Arab J Geosci 6:2727–2743

    Article  Google Scholar 

  • Carrara A, Cardinalli M, Detti R, Guzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16:427–445

    Article  Google Scholar 

  • Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44:949–962

    Article  Google Scholar 

  • Chacón, J., Irigaray, C., Fernández, T., El Hamdouni, R. (2006). Engineering geology maps: Landslides and GIS. Bulletin of Eng. Geol. and the Environment, 65:341–411

    Google Scholar 

  • Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65(12):1388–1399

    Google Scholar 

  • Clark WAV, Hoskin PL (1986) Statistical methods for geographers. Wiley, New York

    Google Scholar 

  • Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza Valley Case Study (Italian Northern Apennines). Environ Geol 50:941–961

    Article  Google Scholar 

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Shuster RL (eds) Landslides: investigation and mitigation. Transp Res Board, Spec Rep 247, pp 36–75

  • Dag S (2007) Landslide susceptibility analysis of Cayeli Region (Rize) by Statistical methods, Ph.D. thesis (unpublished), Karadeniz Technical University

  • Das O, Sonmez H, Gokceoglu C, Nefeslioglu H (2013) Influence of seismic acceleration on landslide susceptibility maps: a case study from NE Turkey (the Kelkit Valley). Landslides 10(4):433–454

    Article  Google Scholar 

  • Demir G, Aytekin M, Akgun A, İkizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65:1481–1506

    Article  Google Scholar 

  • Eastman JR (2004) IDRISI Kilimanjaro: guide to GIS and image processing. Clark Labs, Clark University, Worcester, p 328

    Google Scholar 

  • Erener A, Duzgun HS (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2004) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874

    Article  Google Scholar 

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage W (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102:85–98

    Article  Google Scholar 

  • General Directorate of Mineral Research and Explorations (2005). 1:25.000 Scale Landslide inventory sheets. MTA Publications, Ankara.

  • General Directory of Meteorological Services of Turkey (2013). http://www.dhmi.gov.tr Accessed 16 Dec 2013

  • Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide susceptibility map of its near vicinity. Eng Geol 81:65–83

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (1989) Applied regression analysis. Wiley, New York

  • Kavzoglu T, Sahin EK, Colkesen I (2013) Landslide susceptibility mapping using GIS- based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. doi:10.1007/s10346-013-0391-7, 1–10

  • Kelarestaghi A, Ahmadi H (2009) Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran. Arab J Geosci 2:95–101

    Article  Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine Slovenia. Geomorphology 71(1–4):17–28

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113

    Article  Google Scholar 

  • Lee S, Choi J, Min K (2004) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sens 25:2037–2052

    Article  Google Scholar 

  • Nefeslioglu AH, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94(3):401–418

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012a) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannonâs entropy and GIS. Appl Mech Mater 225:486–491

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012b) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2013) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk 4(2):93–118

    Article  Google Scholar 

  • Pradhan B (2011) Manifestation of an advanced fuzzy logic model coupled with geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18(3):471–493

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3:319–326

    Article  Google Scholar 

  • Pradhan B, Latif ZA, Aman SNA (2012) Application of airborne LiDAR-derived parameters and probabilistic-based frequency ratio model in landslide susceptibility mapping. Appl Mech Mater 225:442–447

    Article  Google Scholar 

  • Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2013) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. doi:10.1007/s12517-012-0807-z, 1–18 (online)

  • Tatar O, Turk T, Gursoy H, Hastaoglu K, Ayazlı E, Poyraz E, Gursoy O, Zabcı C, Demir G, Dikmen U, Akın M, Mesci L, Kocbulut F, Kavak KS, Sezen TF, Polat A (2007). Kelkit vadisi afet bilgi sistemi (KABIS) altyapisinin oluşturulmasi. TMMOB Coğrafi Bilgi Sistemleri Kongresi, 30 Ekim-2 Kasım, Trabzon 2007

  • Tien Bui D, 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

    Article  Google Scholar 

  • Van Westen CJ (1994) Remote sensing and geographic information systems for geological hazard mitigation. ITC-J 4:393–399

    Google Scholar 

  • Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO Pres, Paris, p 63

    Google Scholar 

  • Van Westen, C.J., Castellanos Abella, E.A., Sekhar, L.K. (2008) Spatial data for landslide susceptibility, hazards and vulnerability assessment : an overview. In: Engineering geology, 102 (3-4):112–131

  • Xu C, Xu X (2013) Controlling parameter analyses and hazard mapping for earthquake-triggered landslides: an example from a square region in Beichuan County, Sichuan Province, China. Arab J Geosci 6:3827–3839

    Article  Google Scholar 

  • Yetkil V (2009) GOlova (Sivas) Guneydogusunda Kuzey Anadolu Fay Zonu’nun Neotektonik Ozellikleri, Thesis, C.U. FBE Sivas

Download references

Acknowledgments

Cumhuriyet University Scientific Research Projects (CUBAP) are thanked for their support of this study.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Ondokuz Mayıs University, Samsun, Turkey

    Gokhan Demir

  2. Department of Civil Eng. & Arch, University of Bahrain, Isa Town, Kingdom of Bahrain

    Mustafa Aytekin

  3. Department of Geological Engineering, KTU, Trabzon, Turkey

    Aykut Akgun

Authors
  1. Gokhan Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustafa Aytekin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aykut Akgun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aykut Akgun.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Demir, G., Aytekin, M. & Akgun, A. Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey). Arab J Geosci 8, 1801–1812 (2015). https://doi.org/10.1007/s12517-014-1332-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-014-1332-z

Keywords

Search

Navigation