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Landslide susceptibility map** at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms

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Abstract

This study aims to investigate the performances of different training algorithms used for an artificial neural network (ANN) method to produce landslide susceptibility maps. For this purpose, Ovacık region (southeast of Karabük Province), located in the Western Black Sea Region (Turkey), was selected as the study area. A total of 196 landslides were mapped, and a landslide database was prepared. Topographical elevation, slope angle, aspect, wetness index, lithology, and vegetation index parameters were taken into account for the landslide susceptibility analyses. Two different ANN structures, which were composed of single and double hidden layers, were applied to compare the effects of the ANN. Four different training algorithms, namely batch back-propagation, quick propagation, conjugate gradient descent (CGD), and Levenberg–Marquardt, were used for the training stage of the ANN models. Thus, eight different landslide susceptibility maps were produced for the study area using different ANN structures and algorithms. In order to assess the effects and spatial performances of the considered training algorithms on the ANN models, the relative operating characteristics (ROC) and relation value (rij) approaches were used. The susceptibility map produced by CGD1 has the highest AUC (0.817) and rij values (0.972). Comparison of the susceptibility maps indicated that CGD training algorithm is the slowest one among the other algorithms, but this algorithm showed the highest performance on the results.

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References

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summery review and new perspective. Bull Eng Geol Env 58(1):28–44

    Article  Google Scholar 

  • Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. Catena 120:149–162

    Article  Google Scholar 

  • Arnone E, Francipane A, Scarbaci A, Puglisi C, Noto LV (2016) Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility map**. Env Model and Software 84:467–481

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-based susceptibility map** with comparisons of results from two methods and verifications. Eng Geol 81(4):432–445

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29

    Google Scholar 

  • Chen J, Zenga Z, Jiang P, Tang H (2015) Deformation prediction of landslide based on functional network. Neurocomputing 149:151–157

    Article  Google Scholar 

  • Choi J, Oh H, Won J, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility map**. Env Earth Sci 60:473–483

    Article  Google Scholar 

  • Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23

    Article  Google Scholar 

  • Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for map** landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). CATENA 113:236–250

    Article  Google Scholar 

  • Dagdelenler G, Nefeslioglı HA, Gokceoglu C (2015) Modification of seed cell sampling strategy for landslide susceptibility map**: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bull Eng Geol Env 75:575–590

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ding L, Matthews J (2009) A contemporary study into the application of neural network techniques employed to automate CAD/CAM integration for 130 die manufacture. Comput Ind Eng 57:1457–1471

    Article  Google Scholar 

  • Ercanoglu M (2005) Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks. Nat Haz Earth Sys Sci 5:979–992

    Article  Google Scholar 

  • Ercanoglu M, Dagdelenler G, Ozsayın E, Alkevli T, Sonmez H, Ozyurt NN, Kahraman B, Ucar İ, Cetinkaya S (2016) Application of Chebyshev theorem to data preparation in landslide susceptibility map** studies: an example from Yenice (Karabuk, Turkey) region. J Mt Sci 13(11):1923–1940

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fahlman SE (1988) Faster-Learning Variations on Back-Propagation: An Empirical Study. In Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, pp 1–17

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

    Article  Google Scholar 

  • García-Rodríguez MJ, Malpica JA (2010) Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an artificial neural network model. Nat Haz Earth Sys Sci 10:1307–1315

    Article  Google Scholar 

  • Glade T, Crozier MJ (2005) Landslide hazard and risk—Concluding comment and perspectives. In: Glade T, Anderson M, Crozier M (eds) Landslide hazard and risk. Wiley, Chichester, pp 767–774

    Chapter  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 H (2008) Implementation of reconstructed geomorphologic units in landslide susceptibility map**: the Melen Gorge (NW Turkey). Nat Haz 46(3):323–351

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzonne F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184

    Article  Google Scholar 

  • Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993

    Article  Google Scholar 

  • Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS, Boston

    Google Scholar 

  • Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility map**: a case study at Yenice (Karabuk, NW Turkey). Nat Haz 63:1157–1179

    Article  Google Scholar 

  • Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand 49:2379

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kawabata D, Bandidas J (2009) Landslide susceptibility map** using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology 113:97–109

    Article  Google Scholar 

  • Lan HX, Zhou CH, Wang LJ, Zhang HY, Li RH (2004) Landslide hazard spatial analysis and prediction using GIS in the **aojiang watershed, Yunnan, China. Engineering Geology 76(1–2):109–128

    Article  Google Scholar 

  • Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L (2005) The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 38(5):404–415

    Article  Google Scholar 

  • Lee CF, Ye H, Yeung MR, Shan X, Chen G (2001) AIGIS-based methodology for natural terrain landslide susceptibility map** in Hong Kong. Episodes 24(3)

  • Lee S, Ryu J-H, Min K, Won J-S (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Landf 28:1361–1376

    Article  Google Scholar 

  • Lee S, Ryu J-H, Min K, Won J-S, Park H-J (2004) Determination and application of the weights for landslide susceptibility map** using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Li Y, Chen G, Zhou G, Zheng I (2012) Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network. Nat Haz Earth Syst Sci 12:2719–2729

    Article  Google Scholar 

  • Lippmann R (1987) An introduction to computing with neural nets. ASSP Mag, IEEE

    Book  Google Scholar 

  • McCulloch WS, Pitts W (1990) A logical calculus of the ideas immanent in nervous activity. Bull Math Biol 52(1/2):99–115

    Article  Google Scholar 

  • MTA (2002) Geological map of Turkey. General directorate of mineral research and exploration, Ankara

    Google Scholar 

  • Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3(2):159–174

    Article  Google Scholar 

  • Nauck D, Klawonn F, Kruse R (1997) Foundations of neuro-fuzzy systems. Wiley, New York. ISBN 0471971510

    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 

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility map** using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Env Earth Sci 68:1443–1464

    Article  Google Scholar 

  • Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model. Earth Sci Front 14(6):143–152

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithnera MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34:216–235

    Article  Google Scholar 

  • Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC (2013) Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Haz 65:315–330

    Article  Google Scholar 

  • Romer C, Ferentinou M (2016) Shallow landslide susceptibility assessment in a semiarid environment- A Quaternary catchment of KwaZulu-Natal, South Africa. Eng Geol 201:29–44

    Article  Google Scholar 

  • Rosenblatt F (1958) Theperceptron: aprobabilistic model for information storage and organization in the brain. Psychol Rev 6:386–408

    Article  Google Scholar 

  • Ross TJ (1995) Fuzzy logic with engineering applications. Mc-Graw-Hill, New Mexico

    Google Scholar 

  • Rumelhart D, Hinton G, Williams R (1985) Learning internal representations by error propagation. ICS Report 8506

  • Sejnowski T, Rosenberg C (1987) Parallel networks that learn to pronounce English text. Comp Syst 1(1):145–168

    Google Scholar 

  • Shanthi D, Sahoo G, Saravanan N (2009) Evolving connection weights of artificial neural networks using genetic algorithm with application to the prediction of stroke disease. Int J Soft Comput 4:95–102

    Google Scholar 

  • Sooters R, Van Westen CJ (1996) Slope stability recognition analysis and zonation. In: Turner AK, Schuster RI (eds) Landslides: investigation and mitigation, transportation research board special report 247. National Academy Press Washington DC 129–177 pp

  • 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 

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

    Article  Google Scholar 

  • Timur E, Aksay A (2002) 1/100.000 scaled geological maps of Turkey, Zonguldak F29 Quadrangle. MTA Institution Publication

  • Van Den Eeckhaut M, Hervás J (2012) State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk. Geomorphology 139–140:545–558

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement, types and processes. In: Schuster RL, Krizek RJ (eds) Landslides, analysis and control, special report 176: Transportaion research board. National Academy of Sciences, Washington DC, pp 11–33

    Google Scholar 

  • Wu X, Niu R, Ren F, Peng L (2013) Landslide susceptibility map** using rough sets and backpropagation neural networks in the Three Gorges, China. Env Earth Sci 70:1307–1318

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility map**: 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 (2009a) Landslide susceptibility map** using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comp and Geosci 35:1125–1138

    Article  Google Scholar 

  • Yilmaz I (2009b) Comparison of landslide susceptibility map** methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Env Earth Sci 61:821–883

    Article  Google Scholar 

  • Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility map** by conditional probability and artificial neural networks. Env Earth Sci 60:505–519

    Article  Google Scholar 

  • Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility map** at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6:2873–2888

    Article  Google Scholar 

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Acknowledgements

This research was supported by Hacettepe University Scientific Researches Coordination Section (Project No: 735). The authors would also like to thank Mr. Alphan Haktanır and Mr. Arda Öncü for their logistic support during the field studies.

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Authors and Affiliations

  1. Geological Engineering Department, Hacettepe University, Beytepe, 06800, Ankara, Turkey

    Aslı Can, Gulseren Dagdelenler, Murat Ercanoglu & Harun Sonmez

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  1. Aslı Can
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  2. Gulseren Dagdelenler
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Correspondence to Murat Ercanoglu.

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Can, A., Dagdelenler, G., Ercanoglu, M. et al. Landslide susceptibility map** at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bull Eng Geol Environ 78, 89–102 (2019). https://doi.org/10.1007/s10064-017-1034-3

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