Skip to main content
Springer Nature Link
Log in

Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models

  • Original Article
  • Published:
Environmental Geology

Abstract

Landslides are very common natural problems in the Black Sea Region of Turkey due to the steep topography, improper use of land cover and adverse climatic conditions for landslides. In the western part of region, many studies have been carried out especially in the last decade for landslide susceptibility mapping using different evaluation methods such as deterministic approach, landslide distribution, qualitative, statistical and distribution-free analyses. The purpose of this study is to produce landslide susceptibility maps of a landslide-prone area (Findikli district, Rize) located at the eastern part of the Black Sea Region of Turkey by likelihood frequency ratio (LRM) model and weighted linear combination (WLC) model and to compare the results obtained. For this purpose, landslide inventory map of the area were prepared for the years of 1983 and 1995 by detailed field surveys and aerial-photography studies. Slope angle, slope aspect, lithology, distance from drainage lines, distance from roads and the land-cover of the study area are considered as the landslide-conditioning parameters. The differences between the susceptibility maps derived by the LRM and the WLC models are relatively minor when broad-based classifications are taken into account. However, the WLC map showed more details but the other map produced by LRM model produced weak results. The reason for this result is considered to be the fact that the majority of pixels in the LRM map have high values than the WLC-derived susceptibility map. In order to validate the two susceptibility maps, both of them were compared with the landslide inventory map. Although the landslides do not exist in the very high susceptibility class of the both maps, 79% of the landslides fall into the high and very high susceptibility zones of the WLC map while this is 49% for the LRM map. This shows that the WLC model exhibited higher performance than the LRM model.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Akgun A, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51(8):1377–1387

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44

    Article  Google Scholar 

  • Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to landsliding in the central Appennines, Italy. Comput Geosci 24(4):373–385

    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 65:15–31

    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 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslide in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparision of results from two methods and verifications. Eng Geol 81:432–445

    Article  Google Scholar 

  • Barredo JI, Benavides A, Hervas J, van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Trijana basin. Gran Canaria Island, Spain. Int J Appl Earth Obser Geoinf 2:9–23

    Article  Google Scholar 

  • Bonham-Carter GF (1996) Geographical information systems for geoscientist, modeling with GIS. Pergamon Press, Oxford

    Google Scholar 

  • Brabb EE (1984) Innovative approaches to landslide hazard and risk mapping. In: Proceedings of the 4th international symposium on landslides, vol 1. Canadian Geotechnical Society, Toronto, pp 307–324

  • Brabb EE, Pampeyan EH (1972) Preliminary map of landslide deposits in San Mateo County, California. US Geological Survey Miscellaneous Field Studies, Map MF-360, scale 1:62.500 (reprinted in 1978)

  • Bulut F, Boynukalin S, Tarhan F, Ataoglu E (1995) The reasons of the landslides occurred in the east of the Findikli (Rize) district. In: Proceedings of the 2nd national landslide symposium, Adapazarı, pp 143–152

  • Bulut F, Boynukalın S, Tarhan F, Ataoğlu E (2000) Reliability of landslide isopleth maps. Bull Eng Geol Environ 58:95–98

    Article  Google Scholar 

  • Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessment of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses. Geomorphology 72:250–271

    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 Proc Land 16(5):427–445

    Article  Google Scholar 

  • Carstensen LW (1987) A measure of similarity for cellular maps. Am Cartogr 14(4):345–358

    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 

  • Chacon J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65:341–411

    Article  Google Scholar 

  • Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information systems: a case study. Can Geotech J 38:911–923

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modelling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • Dai FC, Lee CF, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391

    Article  Google Scholar 

  • Dobrovolny E (1971) Landslide susceptibility in and near anchorage as interpreted from topographic and geologic maps, in the great Alaska earthquake of 1964-Geology volume. Publication 1603. U.S. Geological Survey Open File Report 86-329, National Research Council, Committee on the Alaska Earthquake, National Academy of Sciences, USA, pp 735–745

  • Donati L, Turrini MC (2002) An objective method to rank the importance of the parameters predisposing to landslides with the GIS methodology: application to an area of the Appennines (Valnerina; Perugia, Italy). Eng Geol 63:277–289

    Article  Google Scholar 

  • Duman TY, Can T, Emre O, Kecer M, Dogan A, Ates S, Durmaz S (2005) Landslide inventory of Northwestern Anatolia. Eng Geol 77:99–114

    Article  Google Scholar 

  • Eastman R (1999) Multi-criteria evaluation and GIS. In: Longley PA, Goodchild MF, Maguire DJ, Rhind DW (eds) Geographical information systems, Chap 35. Wiley, New York

    Google Scholar 

  • Eastman R (2003) IDRISI Kilimanjaro: guide to GIS and image processing. Clark Labs, Clark University, Worcester, USA

    Google Scholar 

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229–250

    Article  Google Scholar 

  • Ermini L, Filippo C, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66:327–343

    Article  Google Scholar 

  • Friedman D, Diaconis P (1981) On the histograms a density estimator L2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 57:453–476

    Article  Google Scholar 

  • General Directory of Meteorological Services of Turkey (2006) www.meteor.gov.tr/2006/zirai/zirai-calismalar (connected:18.10.2006)

  • Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44:147–161

    Article  Google Scholar 

  • Gokceoglu C, Sonmez H, Ercanoglu M (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindağ (settlement) region in Turkey. Eng Geol 55:277–296

    Article  Google Scholar 

  • 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 

  • Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabanosa River Basin, Venezuela. Eng Geol 78:11–27

    Article  Google Scholar 

  • Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415

    Article  Google Scholar 

  • Guzetti F, Cardinali M, Reichenbach P, Carrara A (2000) Comparing landslides maps: a case study in the Upper Tiber basin, Central Italy. Environ Manage 25(3):247–263

    Article  Google Scholar 

  • Guzetti F, Carrarra A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

  • Ildir B (1995) Türkiyede heyelanlarin dagilimi ve afetler yasası ile ilgili uygulamalar. In: Onalp A (ed) Proceedings of the 2nd national landslide symposium, Turkey, Sakarya University, pp 1–9

  • Irigaray C, Fernandez T, El Hamdouni R, Chacon J (1999) Verification of landslide susceptibility mapping. A case study. Earth Surf Proc Land 24:537–544

    Google Scholar 

  • Irigaray C, Fernandez T, El Hamdouni R, Chacon J (2006) Evaluation and validation of landslide-susceptibility obtained by GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards. doi:10.1007/s11069-006-9027-8

  • Jiang H, Eastman JR (2000) Application of fuzzy measures in multicriteria evaluation in GIS. Int J Geog Inform Sci 14:173–184

    Article  Google Scholar 

  • Jibson WR, Edwin LH, John AM (2000) A method for producing digital probabilitic seismic landslide hazard maps. Eng Geol 58:271–289

    Article  Google Scholar 

  • Kobashi S, Suzuki M (1988) Hazard index for the judgment of slope stability in the Rokko Mountain region. In: Proceedings of the interpraevent, vol 1. Grazz, Austria, pp 223–233

  • Komac M (2006) A landslide susceptibility model using analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74:17–28

    Article  Google Scholar 

  • Korkmaz S, Gedik A (1988) Geology of the region between Rize-Findikli-Camlihemsin districts and petroleum occurrences. Geol Eng J 32:5–15 (in Turkish)

    Google Scholar 

  • Lee S (2004) Application of likelihood ratio and logistic regression model for landslide susceptibility mapping using GIS. Environ Manage 34:223–232

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

    Article  Google Scholar 

  • Lee S, Dan NT (2005) Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ Geol 48:778–787

    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, Ryu JH, Lee MJ, Won JS (2003) Landslide susceptibility analysis using artificial neural network at Boun, Korea. Environ Geol 44:820–833

    Article  Google Scholar 

  • Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–219

    Article  Google Scholar 

  • Lee S, Ryu JH, Won JS, Park H (2004b) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855

    Article  Google Scholar 

  • Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New York

    Google Scholar 

  • Ohlmacher CG, Davis CJ (2003) Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343

    Article  Google Scholar 

  • Ott L, Larson RF, Mendenhall W (1983) Statistics: a tool for the social sciences. Duxbury Press, Boston

    Google Scholar 

  • Oztekin B, Topal T (2005) GIS-based detachment susceptibility analyses of a cut slope in limestone, Ankara, Turkey. Environ Geol 49:124–132

    Article  Google Scholar 

  • Pachauri AK, Pant M (1992) Landslide hazard mapping based on geological attributes. Eng Geol 32:81–100

    Article  Google Scholar 

  • Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36(3–4):325–334

    Article  Google Scholar 

  • Radbruch DH (1970) Map of relative amounts of landslides in California. US Geological Survey Open-File Report 70-1485, 36 pp, map scale 1:500.000. US Geological Survey Open-File Report, 85-585

  • Remondo J, Gonzalez A, Diaz de Teran JR, Cendrero A, Fabbri A, Cheng CF (2003) Validation of landslide susceptibility maps: examplesand applications from a case study in Northern Spain. Nat Hazards 30(3):437–449

    Article  Google Scholar 

  • Saaty TL (1980) The analytical hierarchy process. McGraw Hill, New York

    Google Scholar 

  • Saaty TL (2000) The fundamentals of decision making and priority theory with the analytic hierarchy process, vol VI, 2nd edn. RWS Publications, Pitsburg

  • Saaty TL, Vargas GL (2001) Models, methods, concepts and applications of the analytic hierarchy process. Kluwer, Boston

    Google Scholar 

  • Scott DW (1979) On optimal and data-base histograms. Biomeditrika 66(3):605–610

    Article  Google Scholar 

  • Sener B, Suzen ML, Doyuran V (2006) Landfill site selection by using geographic information systems. Environ Geol 49:376–388

    Article  Google Scholar 

  • Shou KJ, Wang CF (2003) Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan. Eng Geol 68:237–250

    Article  Google Scholar 

  • Siddiqui MZ, Everett JW, Vieux BE (1996) Landfill sitting using geographic information systems: a demonstration. J Environ Eng 122(6):515–523

    Article  Google Scholar 

  • Soeters R, Van Westen CJ (1996) Slope instability recognition analysis and zonation. In: Turner KT, Schuster RL (eds) Landslides: investigation and mitigation. Transportation Research Board National Research Council, Special Report No: 247, Washington, DC, pp 129–177

  • Suzen ML, Doyuran V (2004a) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey. Eng Geol 71:303–321

    Article  Google Scholar 

  • Suzen ML, Doyuran V (2004b) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679

    Article  Google Scholar 

  • USGS (1993) USCS data user guide 5 for DEM’s, ftp:// mapping.usgs.gov /pub /ti /DEM/demguide (connected: 12.05.2006)

  • Van Westen CJ (1997) Statistical landslide analysis. ILWIS 2.1. for windows application quide. ITC Publication, Enschede

    Google Scholar 

  • Van Westen CJ, Bonilla JBA (1990) Mountain hazard analysis using PC-based GIS. 6th IAEG Congress, vol 1. Balkema, Rotterdam, pp 265–271

    Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides analysis and control. Special Report, vol 176. Trasportation Research Board, National Academy of Sciences, New York, pp 12–33

    Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparision of logistic regression and neural networks methods in a moderate scale study, Hendek region (Turkey). Eng Geol 79:251–266

    Article  Google Scholar 

  • Zhou G, Esaki T, Mitani Y, Xie M, Mori J (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dr. Suat Boynukalın, Prof. Fikret Tarhan and Mr. Erhan Ataoglu for their help in the field surveys and Prof. Necdet Turk for his valuable criticism of the manuscript. In addition, special thanks are due to Mr. Ozgur Akyurek for his great efforts to prepare the digital data in GIS environment. The authors are also grateful to one anonymous reviewer for their valuable comments on the manuscript.

Author information

Authors and Affiliations

  1. Geological Engineering Department, Dokuz Eylul University, Izmir, Turkey

    Aykut Akgun

  2. Geological Engineering Department, Gumushane Engineering Faculty, Karadeniz Technical University, Gumushane, Turkey

    Serhat Dag

  3. Geological Engineering Department, Karadeniz Technical University, Trabzon, Turkey

    Fikri Bulut

Authors
  1. Aykut Akgun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Serhat Dag
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fikri Bulut
    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

Akgun, A., Dag, S. & Bulut, F. Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54, 1127–1143 (2008). https://doi.org/10.1007/s00254-007-0882-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00254-007-0882-8

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy