Skip to main content

Advertisement

SpringerLink
Log in

Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey)

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides in the period of 1955–2007 were recognised using different data sources. A ‘Sampling Circle’ approach was proposed to define shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses, the method of artificial neural networks was implemented. According to the performance analyses conducted using the training and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100 years were produced.

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
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Aldrich C, Reuter MA, Deventer JSJ (1994) The application of neural nets in the metallurgical industry. Miner Eng 7:789–809

    Google Scholar 

  • Balducci V (2009) Rainfall thresholds for the initiation of landslides. Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica. http://rainfallthresholds.irpi.cnr.it

  • Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 43:3–31

    Article  Google Scholar 

  • Baum RL, Coe JA, Godt JW, Harp EL, Reld ME, Savage WZ, Schulz WH, Brlen DL, Chleborad AF, McKenna JP, Michael JA (2005) Regional landslide-hazard assessment for Seattle, Washington, USA. Landslides 2:266–279

    Article  Google Scholar 

  • Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329

    Article  Google Scholar 

  • Bell R, Glade T (2004) Quantitative risk analysis for landslides—examples from Bíldudalur, NW-Iceland. Nat Hazards Earth Syst Sci 4:117–131

    Article  Google Scholar 

  • Bruce M, Dylan K (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci 13:603–606

    Article  Google Scholar 

  • Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann 62(A):23–27

    Article  Google Scholar 

  • Can T, Duman TY, Nefeslioglu HA, Durmaz S, Gokceoglu C, Sonmez H (2005) Earthflows in a small catchment from Eastern Black Sea Region (Turkey): conditional (environmental) factors and susceptibility assessments. International Symposium on Latest Natural Disasters-New Challenges for Engineering Geology, Geotechnics and Civil Protection, Abstract Book, Sofia, Bulgaria, pp 82–83

  • Cardinali M, Reichenbach P, Guzzetti F, Ardizzone F, Antonini G, Galli M, Cacciano M, Castellani M, Salvati P (2002) A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy. Nat Hazards Earth Syst Sci 2:57–72

    Article  Google Scholar 

  • Carrara A, Sorriso-Valvo M, Reali C (1982) Analysis of landslide form and incidence by statistical techniques, southern Italy. Catena 9:35–62

    Article  Google Scholar 

  • Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS based techniques for mapping landslide hazard. http://deis158.deis.unibo.it

  • Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1998) Use of GIS technology in the prediction and monitoring of landslide hazard. In: Carrara A, Guzzetti F (eds) Techniques and tools for mapping natural hazards and risk impact on the developed environment, Proceed. EGS 97, Vienna

  • Carrasco RM, Pedraza J, Martin-Duque JF, Mattera M, Sanz MA, Bodoques JM (2003) Hazard zoning for landslides connected to torrential floods in the Jerte Valley (Spain) by using GIS techniques. Nat Hazards 30:361–381

    Article  Google Scholar 

  • Cascini L (2008) Applicability of landslide susceptibility and hazard zoning at different scales. Eng Geol 102(3–4):164–177

    Article  Google Scholar 

  • Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2:329–342

    Article  Google Scholar 

  • Ceresetti D, Molini G, Creutin JD (2010) Scaling properties of heavy rainfall at short duration: a regional analysis. Water Resour Res 46:W09531. doi:10.1029/2009WR008603

    Article  Google Scholar 

  • Chang TC, Chao RJ (2006) Application of back-propagation networks in debris flow prediction. Eng Geol 85:270–280

    Article  Google Scholar 

  • Chau KT, Sze YL, Fung MK, Wong WY, Fong EL, Chan LCP (2004) Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput Geosci 30(4):429–443

    Article  Google Scholar 

  • Chung CF, Fabbri A (2005) Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. In: Glade T, Anderson M, Crozier MJ (eds) Landslide Hazard and Risk. John Wiley & Sons, pp 139–174

  • 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(7):941–961

    Article  Google Scholar 

  • Coe JA, Michael JA, Crovelli RA, Savage WZ (2000) Preliminary map showing landslide densities, mean recurrence intervals, and exceedance probabilities as determined from historic records, Seattle, Washington. United States Geological Survey Open File Report 00–303

  • Corominas J, Moya J (2008) A review of assessing landslide frequency for hazard zoning purposes. Eng Geol 102(3–4):193–213

    Article  Google Scholar 

  • Corominas J, Copons R, Vilaplana JM, Altimir J, Amigo J (2003) Integrated landslide susceptibility analysis and hazard assessment in the principality of Andorra. Nat Hazards 30:421–435

    Article  Google Scholar 

  • Corominas J, Copons R, Moya J, Vilaplana MJ, Altimir J, Amigo J (2005) Quantitative assessment of the residual risk in a rockfall protected area. Landslides 2:343–357

    Article  Google Scholar 

  • Crovelli RA (2000) Probability models for estimation of number and costs of landslides. United States Geological Survey Open File Report 00–249

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides investigation and mitigation, special report 247, pp 36–75

  • Dag S, Bulut F, Akgun A (2006) Iki degiskenli istatistiksel analiz yontemi ile Cayeli (Rize) ve cevresindeki heyelanlarin degerlendirilmesi. TMMOB Jeoloji Muhendisleri Odasi 1. Heyelan Sempozyumu, Bildiriler Kitabi, Trabzon, p 84

  • DMI (2008) DMI Genel Mudurlugu. http://www.meteoroloji.gov.tr/index.aspx

  • Ergunay O (1999) A perspective of disaster management in Turkey: issues and prospects. Urban settlements and natural disasters. Proceedings of UIA Region II Workshop, pp 1–9

  • Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343

    Article  Google Scholar 

  • Fabbri AG, Chung CJF, Cendrero A, Remondo J (2003) Is prediction of future landslides possible with a GIS? Nat Hazards 30:487–499

    Article  Google Scholar 

  • Fahlman SE (1988) An empirical study of learning speed in back-propagation. Technical Report, CMU-CS-88-162, Carnegie-Mellon University

  • Fell R, Corominas J, Bonard C, Cascini L, Leroi E, Savage WZ (2008a) Commentary guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3–4):85–98

    Article  Google Scholar 

  • Fell R, Corominas J, Bonard C, Cascini L, Leroi E, Savage WZ (2008b) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102(3–4):99–111

    Article  Google Scholar 

  • Fu L (1995) Neural networks in computer intelligence. McGraw-Hill, New York

    Google Scholar 

  • Gokceoglu C, Sezer E (2009) A statistical assessment on international landslide literature (1945–2008). Landslides 6:345–351

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gorum T, Gonencgil B, Gokceoglu C, Nefeslioglu HA (2008) Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Nat Hazards 46(3):323–351

    Article  Google Scholar 

  • Guven IH (1998) Trabzon-C30 ve D30 Paftalari. 1/100,000 Olcekli Acinsama Nitelikli Turkiye Jeoloji Haritalari 59, MTA Genel Mudurlugu, Ankara

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299

    Article  Google Scholar 

  • Hassoun MH (1995) Fundamentals of Artificial Neural Networks. MIT Press, Cambridge, MA

  • Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. Proceedings of the first IEEE International Conference on Neural Networks, San Diego, CA, USA, pp 11–14

  • Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, Reading, MA

    Google Scholar 

  • Hong Y, Adler R, Huffman G (2006) Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys Res Lett 33:L22402

    Article  Google Scholar 

  • Hush DR (1989) Classification with neural networks: a performance analysis. Proceeding of the IEEE International Conference on Systems Engineering, Dayton. Ohio, USA, pp 277–280

  • IAEG (Commision on Landslides) (1990) Suggested nomenclature for landslides. Bull Int Assoc Eng Geol 41:13–16

    Article  Google Scholar 

  • Ildir B (1995) Turkiyede heyelanlarin dagilimi ve afetler yasasi ile ilgili uygulamalar. 2. Ulusal Heyelan Sempozyumu Bildiriler Kitabi, Sakarya, pp 1–9

  • Jacobs RA (1988) Increased rates of convergence through learning rate adaptation. Neural Network 1:295–307

    Article  Google Scholar 

  • Jaiswal P, Van Westen CJ, Jetten V (2010) Quantitative landslide hazard assessment along a transportation corridor in southern India. Eng Geol 116:236–250

    Article  Google Scholar 

  • Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236

    Article  Google Scholar 

  • Kalafat D, Gunes Y, Kara M, Deniz P, Kekovali K, Kuleli HS, Gulen L, Yilmazer M, Ozel NM (2007) Butunlestirilmis Homojen Turkiye Deprem Katalogu (1900–2005; M≥4.0). Bogazici Universitesi, Kandilli Rasathanesi ve Deprem Arastirma Enstitusu, No. 977, Istanbul

  • Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Rem Sens 18:711–725

    Article  Google Scholar 

  • Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366

    Article  Google Scholar 

  • Karsli F, Atasoy M, Yalcin A, Reis S, Demir O, Gokceoglu C (2009) Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey). Environ Monit Assess 156:241–255

    Article  Google Scholar 

  • Kiss R (2004) Determination of drainage network in digital elevation model, utilities and limitations. J Hungarian Geomathematics 2:16–29

    Google Scholar 

  • Klimasauskas CC (1993) Applying neural networks. In: Trippi RR, Turban E (eds) Neural Networks in Finance and Investigating. Probus, Cambridge

    Google Scholar 

  • Lateltin O, Haemmig C, Raetzo H, Bonnard C (2005) Landslide risk management in Switzerland. Landslides 2:313–320

    Article  Google Scholar 

  • Lee S, Evangelista DG (2006) Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst Sci 6:687–695

    Article  Google Scholar 

  • Lee S, Choi J, Min K (2002a) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43:120–131

    Article  Google Scholar 

  • Lee S, Chwae U, Min K (2002b) Landslide susceptibility mapping by correlation between topography and geological structure the Janghung area, Korea. Geomorphology 46:149–162

    Article  Google Scholar 

  • Lee S, Ryu JH, Won JS, Park HJ (2004) 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, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–220

    Article  Google Scholar 

  • Li G, Alnuweiri H, Wu W (1993) Acceleration of back-propagation through initial weight pre-training with Delta rule. In: Proceedings of an International Joint Conference on Neural Networks, San Francisco, CA, pp 580–585

  • Meijerink AMJ (1988) Data acquisition and data capture through terrain mapping units. ITCJ 1:23–44

    Google Scholar 

  • Messer K, Kittler J (1998) Choosing an optimal neural network size to aid search through a large image database. Proceedings of Ninth British Machine Vision Conference (BMVC98). University of Southampton, UK, pp 235–244

  • Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3(2):159–173

    Article  Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191

    Article  Google Scholar 

  • Negnevitsky M (2002) Artificial intelligence—a guide to intelligent systems. Addison-Wesley Co., Great Britain

  • Ohlmacher CG (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91(2–4):117–134

    Article  Google Scholar 

  • Olaya V (2004) A gentle introduction to SAGA GIS. http://sourceforge.net/projects/saga-gis/files

  • Paola JD (1994) Neural network classification of multispectral imagery. MSc Thesis, The University of Arizona, USA

  • Park S (2004) Annual solar radiation. http://www.esri.com

  • Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

    Article  Google Scholar 

  • Remondo J, Bonachea J, Cendrero A (2005) A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment. Landslides 2:321–328

    Article  Google Scholar 

  • Ripley BD (1993) Statistical aspects of neural networks. In: Barndorff-Nielsen OE, Jensen JL, Kendall WS (eds) Networks and chaos—statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123

    Google Scholar 

  • Romeo RW, Floris M, Veneri F (2006) Area-scale landslide hazard and risk assessment. Environ Geol 51(1):1–13

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel Distributed Processing 1, pp 318–362

  • Schmidt W, Raudys S, Kraaijveld M, Skurikhina M, Duin R (1993) Initialization, backpropagation and generalization of feed-forward classifiers. In: Proceeding of the IEEE International Conference on Neural Networks, pp 598–604

  • Singhroy V (2005) Remote sensing of landslides. In: Glade T, Anderson M, Crozier MJ (eds) Landslide hazard and risk. John Wiley & Sons, pp 469–492

  • Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis and zonation. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation. Transp Res. Board, Nat Res. Counc Spec Rep 247:129–177

  • Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235

    Article  Google Scholar 

  • Suzen ML, Doyuran V (2004) 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 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  Google Scholar 

  • Swingler K (1996) Applying neural networks: a practical guide. Academic, New York

    Google Scholar 

  • Tan O, Tapirdamaz MC, Yoruk A (2008) The earthquake catalogues for Turkey. Turk J Earth Sci 17:405–418

    Google Scholar 

  • Tarhan F (1991) Dogu Karadeniz heyelanlarina genel bir bakis. 1. Ulusal Heyelan Sempozyumu Bildiriler Kitabi, Trabzon, pp 38–63

  • Telesca L, Lapenna V, Scalcione E, Summa D (2007) Searching for time-scaling features in rainfall sequences. Chaos, Solitons & Fractals 32(1):35–41

    Article  Google Scholar 

  • Trigila A, Iadanza C, Spizzichino D (2010) Quality assessment of the Italian Landslide Inventory using GIS processing. Landslides 7:455–470

    Article  Google Scholar 

  • TUIK (2006) The summary of agricultural statistics 1987–2006. Prime Ministry Republic of Turkey Statistical Institute, ISSN 1300–1213

  • Tunusluoglu MC, Gokceoglu C, Sonmez H, Nefeslioglu HA (2007) An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey). Nat Hazards Earth Syst Sci 7:557–570

    Article  Google Scholar 

  • Wang C (1994) A theory of generalization in learning machines with neural application. PhD Thesis, The University of Pennsylvania, USA

  • Watrous RL (1987) Learning algorithms for connectionist networks: applied gradient methods of nonlinear optimisation. Proceedings of the first IEEE Int Conf on Neural Networks, San Diego 2, pp 619–627

  • Wilson JP, Gallant JC (2000) Terrain analysis principles and applications. John Wiley and Sons, Inc., Canada

    Google Scholar 

  • WP/WLI (International Geotechnical Societies UNESCO Working Party on World Landslide Inventory) (1990) A suggested method for reporting a landslide. Bull Int Assoc Eng Geol 41:5–12

    Article  Google Scholar 

  • Wythoff BJ (1993) Backpropagation neural networks: a tutorial. Chemometr Intell Lab Syst 18:115–155

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35(6):1125–1138

    Article  Google Scholar 

  • Yilmaz BS, Guc AR, Gulibrahimoglu I, Yazici EN, Konak O, Yaprak S, Kose Z (1998) Rize Ilinin Cevre Jeolojisi. MTA Raporu, No. 10068, Ankara

  • Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surface Processes and Landforms 12:47–56

    Article  Google Scholar 

  • Zezere JL, Reis E, Garcia R, Oliviera S, Rodrigues ML, Vieira G, Ferreira AB (2004) Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal). Nat Hazards Earth Syst Sci 4:133–146

    Article  Google Scholar 

  • Zezere JL, Garcia RAC, Oliveira SC, Reis E (2008) Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon (Portugal). Geomorphology 94(3–4):467–495

    Article  Google Scholar 

  • Zupan J, Gasteiger J (1993) Neural networks for chemists: an introduction. VCH, New York

    Google Scholar 

Download references

Acknowledgements

This study was supported by the Hacettepe University Scientific Research Unit (Ankara, Turkey) with the project 07 01 602 006. The authors would like to thank associate professors, Dr. Sebnem Duzgun and Dr. M. Lutfi Suzen for their constructive comments on the manuscript and assistant professor, Dr. Serhat Dag for his support during the field studies.

Author information

Authors and Affiliations

  1. General Directorate of Mineral Research and Exploration, Department of Geological Research, Balgat, 06520, Ankara, Turkey

    Hakan A. Nefeslioglu

  2. Department of Geological Engineering, Applied Geology Division, Hacettepe University, Beytepe, 06800, Ankara, Turkey

    Candan Gokceoglu & Harun Sonmez

  3. Department of Earth Systems Analysis, International Institute for Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, 7500 AA, Enschede, The Netherlands

    Tolga Gorum

Authors
  1. Hakan A. Nefeslioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Candan Gokceoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harun Sonmez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tolga Gorum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hakan A. Nefeslioglu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nefeslioglu, H.A., Gokceoglu, C., Sonmez, H. et al. Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey). Landslides 8, 459–483 (2011). https://doi.org/10.1007/s10346-011-0267-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-011-0267-7

Keywords

Search

Navigation