Abstract
Landslide database construction is one of the most crucial stages of the landslide susceptibility mapping studies. Although there are many techniques for preparing landslide database in the literature, representative data selection from huge data sets is a challenging, and, to some extent, a subjective task. Thus, in order to produce reliable landslide susceptibility maps, data-driven, objective and representative database construction is a very important stage for these maps. This study mainly focuses on a landslide database construction task. In this study, it was aimed at building a representative landslide database extraction approach by using Chebyshev theorem to evaluate landslide susceptibility in a landslide prone area in the Western Black Sea region of Turkey. The study area was divided into two different parts such as training (Basin 1) and testing areas (Basin 2). A total of nine parameters such as topographical elevation, slope, aspect, planar and profile curvatures, stream power index, distance to drainage, normalized difference vegetation index and topographical wetness index were used in the study. Next, frequency distributions of the considered parameters in both landslide and nonlandslide areas were extracted using different sampling strategies, and a total of nine different landslide databases were obtained. Of these, eight databases were gathered by the methodology proposed by this study based on different standard deviations and algebraic multiplication of raster parameter maps. To evaluate landslide susceptibility, Artificial Neural Network method was used in the study area considering the different landslide and nonlandslide data. Finally, to assess the performances of the so-produced landslide susceptibility maps based on nine data sets, Area Under Curve (AUC) approach was implemented both in Basin 1 and Basin 2. The best performances (the greatest AUC values) were gathered by the landslide susceptibility map produced by two standard deviation database extracted by the Chebyshev theorem, as 0.873 and 0.761, respectively. Results revealed that the methodology proposed by this study is a powerful and objective approach in landslide susceptibility mapping.
Similar content being viewed by others
References
Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression multi-criteria decision and likelihood ratio methods: a case study at Izmir Turkey. Landslides 9(1): 93–106. DOI: 10.1007/s10346-011-0283-7
Alkevli T, Ercanoglu M (2011) Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gokcebey (Western Black Sea Region Turkey).Bulletin of Engineering Geology and the Environment 70(4): 607–617. DOI: 10.1007/s10064-011-0353-z
Althuwaynee OF, Pradhan B, Park HJ, et al. (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114: 21–36. DOI: 10.1016/j.catena.2013.10.011
Bai SB, Wang J, Lu GN, et al. (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area China. Geomorphology 115(1): 23–31. DOI: 10.1016/j.geomorph.2009.09.025
Bi R, Schleier M, Rohn J, et al. (2014) Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region China. Environmental Earth Sciences 72(6): 1925–1938. DOI: 10.1007/s12665-014-3100-5
Bluman AG (2004) Elementary Statistics: A Step by Step Approach. McGraw Hill, New York. p 897.
Bui DT, Pradhan B, Lofman O, et al. (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. DOI: 10.1016/j.geomorph.2012.04.023
Chau KT, Chan JE (2005) Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides 2(4): 280–290. DOI: 10.1007/s10346-005-0024-x
Choi J, Oh HJ, Won JS, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility mapping. Environmental Earth Sciences 60(3): 473–483. DOI: 10.1007/s12665-009-0188-0
Clerici A, Perego S, Tellini C, et al. (2006) A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology 50(7): 941–961. DOI: 10.1007/s00254-006-0264-7
Conforti M, Pascale S, Robustelli G, et al. (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria Italy). Catena 113: 236–250. DOI: 10.1016/j.catena.2013.08.006
Corominas J, Van Westen C, Frattini P, et al. (2014) Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment 73(2): 209–263. DOI: 10.1007/s10064-013-0538-8
Cruden DM, Varnes DJ (1996) Landslide Types and Processes. In: Turner AK, Schuster RL (Eds.), Landslides: Investigation and Mitigation, Transportation Research Board, Special Report No. 247, pp: 36–75.
Dagdelenler G, Nefeslioglu HA, Gokceoglu C (2015) Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bulletin of Engineering Geology and the Environment 1–16. DOI: 10.1007/s10064-015-0759-0
Dai FC, Lee CF (2003) A spatiotemporal probabilistic modelling of storm induced shallow landsliding using aerial photographs and logistic regression. Earth Surface Processes & Landforms 28(5): 527–545. DOI: 10.1002/esp.456
Dewitte O, Chung CJ, Cornet Y, et al. (2010) Combining spatial data in landslide reactivation susceptibility mapping: A likelihood ratio-based approach in W Belgium. Geomorphology 122(1): 153–166. DOI: 10.1016/j.geomorph. 2010.06.010
Dowla FU, Rogers LL (1995) Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks. MIT Press, Massachusetts, London. p 241.
Duman TY, Can T, Gokceoglu C, et al. (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area Istanbul Turkey. Environmental Geology 51(2): 241–256. DOI: 10.1007/s00254-006-0322-1
Eastman JR (2012) IDRISI Selva Guide to GIS and Image Processing User’s Guide (Ver17). Clark University Press, Massachusetts, USA.
Ercanoglu M (2005) Landslide susceptibility assessment of SE Bartin (West Black Sea region Turkey) by artificial neural networks. Natural Hazards and Earth System Sciences 5(6): 979–992. DOI: 10.5194/nhess-5-979-2005
Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region Turkey). Engineering Geology 75(3): 229–250. DOI: 10.1016/j.enggeo.2004.06.001
Ercanoglu M, Kasmer Ö, Temiz N (2008) Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment 67(4): 565–578. DOI: 10.1007/s10064-008-0170-1
Erener A, Düzgün HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Environmental Earth Sciences 66(3): 859–877. DOI: 10.1007/s12665-011-1297-0
Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66(1-4): 327–343. DOI: 10.1016/j.geomorph. 2004.09.025
Fell R, Corominas J, Bonnard C, et al. (2008a) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102(3-4): 85–98. DOI: 10.1016/j.enggeo.2008.03.022
Fell R, Corominas J, Bonnard C, et al. (2008b) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102(3-4): 99–111. DOI: 10.1016/j.enggeo.2008.03.014
Fernandez T, Irigaray C, El Hamdouni R, et al. (2003) Methodology for landslide susceptibility mapping by means of a GIS Application to the Contraviesa Area (Granada Spain). Natural Hazards 30(3): 297–308.
Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Engineering Geology 111(1-4): 62–72. DOI: 10.1016/j.enggeo. 2009.12.004
Gómez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin Venezuela. Engineering Geology 78(1-2): 11–27. DOI: 10.1016/j.enggeo.2004.10.004
Gorum T, Gönençgil B, Gökçeoglu C, et al. (2008) Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Natural Hazards 46(3): 323–351. DOI: 10.1007/s11069-007-9190-6
Guzzetti F, Carrara A, Cardinali M, et al. (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. DOI: 10.1016/S0169-555X(99)00078-1
Guzzetti F, Reichenbach P, Ardizzone F, et al. (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1-2): 166–184. DOI:10.1016/j.geomorph.2006.04.007
Guzzetti F, Mondini AC, Cardinali M, et al. (2012) Landslide inventory maps: New tools for an old problem. Earth-Science Reviews 112(1-2): 42–66. DOI:10.1016/j.earscirev.2012.02.001
Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk NW Turkey). Natural Hazards 63(2): 1157–1179. DOI: 10.1007/s11069-012-0218-1
Huang Y, Wanstedt S (1998) The introduction of neural network system and its applications in rock engineering. Engineering Geology 49(3-4): 253–260. DOI: 10.1016/S0013-7952(97)00056-2
Hussin HY, Zumpano V, Reichenbach P, et al. (2016) Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253: 508–523. DOI: 10.1016/j.geomorph.2015.10.030
Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data a DEM from ASTER images and an Artificial Neural Network (ANN).Geomorphology 113(1-2): 97–109. DOI:10.1016/j.geomorph.2009.06.006
Kundu S, Saha AK, Sharma DC, et al. (2013) Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed Himalayas. Journalof the Indian Societyof Remote Sensing 41(3): 697–709. DOI: 10.1007/s12524-012-0255-y
Li Y, Chen G, Tang C, et al. (2012) Rainfall and earthquakeinduced landslide susceptibility assessment using GIS and Artificial Neural Network. Natural Hazards and Earth System Sciences 12(8): 2719–2729. DOI: 10.5194/nhess-12-2719-2012
Marjanovic M, Kovacevic M, Bajat B, et al. (2011) Landslide susceptibility assessments using SVM machine learning algorithm. Engineering Geology 123(3): 225–234. DOI: 10.1016/j.enggeo.2011.09.006
Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province Iran: A comparison between frequency ratio Dempster–Shafer and weights-of-evidence models. Journal of Asian Earth Sciences 61: 221–236. DOI: 10.1016/j.jseaes.2012.10.005
Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology 110(1-2): 11–20. DOI: 10.1016/j.enggeo. 2009.10.001
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. Engineering Geology 97(3-4): 171–191. DOI: 10.1016/j.enggeo.2008.01.004
Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Pearson, Essex.
Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences 37(9): 1264–1276. DOI: 10.1016/j.cageo.2010.10.012
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. Journal of Asian Earth Sciences 64: 180–197. DOI: 10.1016/j.jseaes.2012.12.014
Park S, Choi C, Kim B, et al. (2013) Landslide susceptibility mapping using frequency ratio analytic hierarchy process logistic regression and artificial neural network methods at the Inje area Korea. Environmental Earth Sciences 68(5): 1443–1464. DOI: 10.1007/s12665-012-1842-5
Park I, Lee S (2014) Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area Korea. International Journal of Remote Sensing 35(16): 6089–6112. DOI: 10.1080/01431161.2014.943326
Peng L, Niu R, Huang B, et al. (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area China. Geomorphology 204: 287–301. DOI: 10.1016/j.geomorph.2013.08.013
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. Computers & Geosciences 51: 350–365. DOI: 10.1016/j.cageo. 2012.08.023
Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Science Frontiers 14(6): 143–152. DOI: 10.1016/S1872-5791(08)60008-1
Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based backpropagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems 34(3): 216–235. DOI: 10.1016/j.compenvurbsys.2009.12.004
Remondo J, Gonzalez-Diez A, Teran JRD, et al. (2003) Landslide susceptibility models utilizing spatial data analysis techniques A case study from the lower Deba Valley Guipúzcoa (Spain). Natural Hazards 30(3): 267–279.
Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains Japan. Geomorphology 109(3-4): 108–121. DOI:10.1016/j.geomorph.2009.02.026
San BT (2014) An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya Turkey). International Journal of Applied Earth Observation and Geoinformation 26: 399–412. DOI:10.1016/j.jag.2013.09.010
Santacana N, Baeza B, Corominas J, et al. (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in la Pobla de Lillet area (Eastern Pyrenees Spain). Natural Hazards 30(3): 281–295.
Shahabi H, Khazri S, Ahmad BB, et al. (2014) Landslide susceptibility mapping at Central Zab basin Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena 115: 55–70. DOI:10.1016/j.catena.2013.11.014
Sujatha ER, Kumaravel P, Rajamanickam VG (2012) Landslide susceptibility mapping using remotely sensed data through conditional probability analysis using seed cell and point sampling techniques. Journal of the Indian Society Remote Sensing 40(4): 669–678. DOI: 10.1007/s12524-011-0192-1
Suzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment Turkey. Engineering Geology 71(3-4): 303–321. DOI: 10.1016/S0013-7952(03)00143-1
van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology 102(3-4): 112–132. DOI: 10.1016/j.enggeo.2008.03.010
Walpole RE, Myers RH, Myers SL, et al. (2002) Probability & Statistics for Engineers and Scientists. Prentice Hall, New Jersey, USA. p. 816.
Wang LJ, Sawada K, Moriguchi S (2013) Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences 57: 81–92. DOI: 10.1016/j.cageo.2013.04.006
WP/WLI (The International Geotechnical Societies’ UNESCO Working Party on World Landslide Inventory) (1990) A suggested method for reporting a landslide. Bulletin of the International Association of Engineering Geology 41: 5–12.
WP/WLI (The International Geotechnical Societies’ UNESCO Working Party on World Landslide Inventory) (1993) Multilingual landslide glossary. BiTech Publishers Ltd, British Columbia.
Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae Korea using a decision tree. Engineering Geology 116(3-4): 274–283. DOI: 10.1016/j.enggeo.2010.09. 009
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). Engineering Geology 79(3-4): 251–266. DOI:10.1016/j.enggeo. 2005.02.002
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). Computers & Geosciences 35(6): 1125–1138. DOI: 10.1016/j.cageo.2008.08.007
Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environmental Earth Sciences 60(3): 505–519. DOI: 10.1007/s12665-009-0191-5
Zare M, Pourghasemi HR, Vafakhah M, et al. (2012) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences 6(8): 2873–2888. DOI: 10.1007/s12517-012-0610-x
Author information
Authors and Affiliations
Corresponding author
Additional information
http://orcid.org/0000-0002-3496-214X
http://orcid.org/0000-0002-9409-8285
http://orcid.org/0000-0003-3948-5482
http://orcid.org/0000-0002-7713-7967
http://orcid.org/0000-0002-1561-6233
http://orcid.org/0000-0001-5409-1030
http://orcid.org/0000-0003-4172-5086
http://orcid.org/0000-0002-0597-4707
http://orcid.org/0000-0001-5730-7052
Rights and permissions
About this article
Cite this article
Ercanoglu, M., Dağdelenler, G., Özsayin, E. et al. Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies: an example from Yenice (Karabük, Turkey) region. J. Mt. Sci. 13, 1923–1940 (2016). https://doi.org/10.1007/s11629-016-3880-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-016-3880-z