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Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

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Abstract

This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.

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References

  • Anderberg MR (1973) Cluster analysis for applications. Academic Press, New York

    Google Scholar 

  • ArcGIS (Version 9.1) (2005) Integrated GIS software. ESRI, CA

    Google Scholar 

  • Ayala FJ (1987) Introducción a los riesgos geológicos (Introduction to geological risk). In: Ayala FJ (ed) Riesgos Geológicos (Geological risks). IGME (Instituto Geologico y Minero de Espana, Spain Institute of Geology and Mining), Serie Geología Ambiental, Madrid, Spain, pp 3–19

    Google Scholar 

  • Baeza C (1994) Evaluación de las condiciones de rotura y la movilidad de los deslizamientos superficiales mediante el uso de técnicas de análisis multivariante (Evaluation of the failure conditions and mobility by using multivariate analysis in superficial media). Thesis (PhD). University Politechnique Catalunya (in Spanish)

  • Bai SB, Wang J, Lu GN, Kanevski M, Pozdnoukhov A (2008) GIS-based landslide susceptibility mapping with comparisons of results from machine learning methods process versus logistic regression in Bailongjiang river basin, China. Geophysical Research Abstracts, EGU, 10, A-06367

  • Barredo JJ, Benavides A, Hervas J, Van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Trijana basin. Gran Canaria Island, Spain, JAG2 (1):9–23

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

    Article  Google Scholar 

  • Binaghi E, Luzi L, Madella P, Rampini A (1998) Slope instability zonation: a comparison between certainty factor and fuzzy Dempster-Shafer approaches. Nat Hazards 17:77–97

    Article  Google Scholar 

  • Brabb EE, Pampeyan EH, Bonilla M (1972) Landslide susceptibility in the San Mateo County, California, scale 1:62,500. U.S.G.S. Miscellaneous Field Studies Map, MF344

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth System Sci 5:853–862

    Article  Google Scholar 

  • Burt T, Butcher D (1986) Stimulation from simulation–a teaching model of hillslope hydrology for use on microcomputers. J Geogr Higher Educ 10:23–39

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Process Landform 28:1125–1142

    Article  Google Scholar 

  • Cascini L, Critelli S, Gulla G, Di Nocera S (1991) A methodological approach to landslide hazard assessment: a case history. In: Proceedings of 16th international landslide conference, Balkema, Rotterdam, pp 899–904

  • Chacon J, Irigaray C, Fenandez T (1992) Metodologia para la cartografia regianal de movientos de ladera y riesgos asociados mediante un Sistema de Informacions Geografica (Methodology in mapping of regional landslides by means of Geographical Information System). In: Corominas J, Alonso E (eds) III Simposio Nacional sobre Taludes y laderas Inesstables, La Corruna, Spain, I, pp 121–133

  • Chacón J, Irigaray C, Fernández T (1994) Large to middle scale landslide inventory, analysis and mapping with modeling and assessment of derived susceptibility, hazards and risks in a GIS. In: Proceedings of 7th IAEG congress, Balkema, Rotterdam, pp 4669–4678

  • Chacón J, Irigaray C, Fernández T (1996) From the inventory to the risk analysis: Improvements to a large scale GIS method. In: Chacón J, Irigaray C, Fernández T (eds) Proceedings of 8th international conference and field workshop on landslides, Balkema, pp 335–342

  • Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65(12):1389–1399

    Google Scholar 

  • Chung CF, Fabbri AG, van Westen CJ (1995) Multivariate regression analysis for landslide hazard zonation. In: Carrara A, Guzetti F (eds) Geographical informations systems in assessing natural hazards. Kluwer Publishers, Dordrecht

    Google Scholar 

  • Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364

    Article  Google Scholar 

  • Coraminas J (1987) Criterios para la confeccion de mapes de peligrosidad de movimentos de ladera (Criteria for preparation of landslide hazard maps). In: Ayala FJ (ed) Riesgos Geologicos. IGME (Instituto Geologico y Minero de Espana, Spain Institute of Geology and Mining), Serie Geologia Ambiental, Madrid, Spain, pp 193–201

    Google Scholar 

  • Dai FC, Lee CF, Zhang XH (2001) GIS-based geo-environmental evaluation for urban land-use planning: a case study. Eng Geol 61:257–271

    Article  Google Scholar 

  • Davis TJ, Keller CP (1997) Modeling uncertainty in natural resource analysis using fuzzy sets and Monte-Carlo simulation: slope instability prediction. Int J Geogr Inform Sci 11(5):409–434

    Article  Google Scholar 

  • DeGraff J, Romesburg H (1980) Regional landslide-susceptibility assessment for wildland management: a matrix approach. In: Coates D, Vitek J (eds) Thresholds in geomorphology. George Allen and Unwin, London, pp 401–414

    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 

  • Fernández T, Irigaray C, Hamdouni RE, Chacón J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa Area (Granada, Spain). Nat Hazards 30:297–308

    Article  Google Scholar 

  • Foody GM, Lucas RM, Curran PJ, Honzak M (1996) Estimation of the areal extend of land cover classes that only occur at a sub-pixel level. Can J Remote Sens 22:432–438

    Google Scholar 

  • 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 technique. Eng Geol 44:147–161

    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(1–2):11–27

    Article  Google Scholar 

  • Gorsevski PV, Gessler PE, Jankowski P (2003) Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. J Geogr Syst 5(3):223–251

    Article  Google Scholar 

  • Hall FG, Townshend JR, Engman ET (1995) Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sens Environ 51:138–156

    Article  Google Scholar 

  • Hammond C, Hall D, Miller S, Swetik P (1992) Level I Stability Analysis (LISA) Documentation Version 2.0. General Technical Report INT-285. United States Department of Agriculture, Forest Service Intermountain Research Station, USA

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining inference and prediction. Springer, New York

    Google Scholar 

  • Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of 1st IEEE international conference on neural networks, San Diego, CA, USA, pp 11–14

  • Irigaray C (1995) Movimientos de ladera: inventoria, analisis y cartografaa de susceptibilidad mediante un Sistema de Informacion Geografica. Aplicacion a las zonas de Colmenar (Ma), Rute (Co) y Montefrio (Gr) (Landslides: inventory, susceptibility analysis and mapping by means of a Geographical Information System. Application to the Colmenar (Malaga), Rute (Cordoba) and Montefrio (Granada) sectors). Thesis (PhD). University Granada (in Spanish)

  • Ives JD, Messerli B (1981) Mountain hazard mapping in Nepal: introduction to an applied mountain research project. Mt Res Dev 1(3–4):223–230

    Article  Google Scholar 

  • Jade S, Sarkar S (1993) Statistical models for slope instability classification. Eng Geol 36:91–98

    Article  Google Scholar 

  • Juang CH, Lee DH, Sheu C (1992) Mapping slope failure potential using fuzzy sets. J Geotech Eng ASCE 118(3):475–493

    Article  Google Scholar 

  • Kavzoglu T (2001) An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images. Thesis (PhD). University of Nottingham, School of Geography, 306 pp

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

    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):847–856

    Article  Google Scholar 

  • Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Land 28(12):1361–1376

    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 

  • Lu P, Rosembaum MS (2003) Artificial neural networks and grey systems for the prediction of slope instability. Nat Hazards 30(3):383–398

    Article  Google Scholar 

  • Mahiny AS, Turner BJ (2003) Modeling past vegetation change through remote sensing and GIS: a comparison of neural networks and logistic regression methods. In: Proceedings of the 7th international conference on geocomputation. University of Southampton, UK

  • Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling—a review of hydro- hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Negnevitsky M (2002) Artificial intelligence—a guide to intelligent systems. Addison-Wesley Co., Great Britain, p 394

    Google Scholar 

  • Ottenbacher KJ, Smith PM, Illig SB, Linn RT, Fiedler RC, Granger CV (2001) Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke. J Clin Epidemiol 54:1159–1165

    Article  Google Scholar 

  • Pack RT, Tarboton DG, Goodwin CN (1998) Terrain stability mapping with SINMAP, technical description and users guide for version 1.00. Report Number 4114-0. Terratech Consulting Ltd., Salmon Arm, BC, Canada (www.tclbc.com)

  • Popescu M (1996) From landslide causes to landslide remediation. In: Proceedings of 7th international symposium on landslides, Trondheim, Norway, pp 75–96

  • Rengers N, van Westen CJ, Chacón J, Irigaray C (1998) Draft for the chapter on the application of digital techniques for natural hazard zonation. Report on mapping of natural hazards. International Association of Engineering Geology. Commission no. 1 on Engineering Geological Mapping

  • Rodhe A, Seibert J (1999) Wetland occurrence in relation to topography: a test of topographic indices as moisture indicators. Agric For Meteorol 98–99:325–340

    Article  Google Scholar 

  • Rupke J, Cammeraat E, Seijmonsbergen AC, Van Westen CJ (1988) Engineering geomorphology of the Widentobel catchment, Switzerland: a geomorphological inventory system applied to geotechnical appraisal of the slope stability. Eng Geol 26:33–68

    Article  Google Scholar 

  • Scholkopf B, Smola A, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12:1207–1245

    Article  Google Scholar 

  • Schumacher M, Robner R, Vach W (1996) Neural networks and logistic regression. Part I. Comput Stat Data Anal 21:661–682

    Article  Google Scholar 

  • Seibert J, Bishop KH, Nyberg L (1997) A test of TOPMODEL’s ability to predict spatially distributed groundwater levels. Hydrol Process 11:1131–1144

    Article  Google Scholar 

  • Sendir H, Yilmaz I (2002) Structural, geomorphological and geomechanical aspects of the Koyulhisar landslides in the North Anatolian Fault Zone (Sivas-Turkey). Env Geol 42(1):52–60

    Article  Google Scholar 

  • Singh TN, Kanchan R, Verma AK, Singh S (2003) An intelligent approach for prediction of triaxial properties using unconfined uniaxial strength. Min Eng J 5(4):12–16

    Google Scholar 

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

    Article  Google Scholar 

  • Tangestani MH (2003) Landslide susceptibility mapping using fuzzy gamma operation in a GIS, Kakan catchment area, Iran. Map India 2003, Disaster Management, 7 pp

  • Tax D, Duin E (2002) Uniform object generation for optimizing one-class classifiers. J Mach Learn Res 2:155–173

    Article  Google Scholar 

  • Terlemez İ, Yılmaz A (1980) Ünye-Ordu-Reşadiye-Koyulhisar-Karaçayır-Hafik arasında kalan bölgenin jeolojisi. M.T.A. Report No. 6671 (in Turkish)

  • Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231

    Article  Google Scholar 

  • Van Westen CJ (1993) Application of geographic information systems to landslide hazard zonation. Thesis (PhD). Technical University Delft, ITC publication number 15, ITC, Enschede, The Netherlands, 245 pp

  • Van Westen CJ, Lulie GF (2003) Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology 54(1–2):77–89

    Google Scholar 

  • Van Westen CJ, Soeters R, Sijmons K (2000) Digital geomorphological landslide hazard mapping of the Alpago area, Italy. Int J Appl Earth Observ Geoinform 2(1):51–59

    Article  Google Scholar 

  • Vapnik V (1995) Nature of statistical learning theory. Wiley, New York

    Google Scholar 

  • Ward TJ, Ruh-Ming L, Simons DB (1982) Mapping landslide hazard in forest watershed. J Geotech Eng Div ASCE 108(GT-2):319–324

    Google Scholar 

  • Weier J, Herring D (2005) Measuring vegetation (NDVI and EVI). Earth Observatory Library of NASA. http://earthobservatory.nasa.gov/Library/MeasuringVegetation/

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yilmaz I (2007) GIS based susceptibility mapping of karst depression in gypsum: a case study from Sivas basin (Turkey). Eng Geol 90:89–103

    Article  Google Scholar 

  • Yilmaz I (2008) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68(3):297–306

    Article  Google Scholar 

  • Yilmaz I (2009a) Landslide susceptibility 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 I (2009b) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability (CP) and artificial neural networks (ANN). Environ Earth Sci. doi:10.1007/s12665-009-0191-5

  • Yilmaz I, Keskin I (2007) Sebinkarahisar ve Alucra yöresi heyelanları: Heyelan duyarlılık haritalarının Frekans Oranı (FR) ve Yapay Sinir Ağları (ANN) Modelleri kullanılarak hazırlanması. Cumhuriyet University BAP Project Report, M-342, 90 pp (in Turkish with English abstract)

  • Yilmaz I, Yildirim M (2006) Structural and geomorphological aspects of the Kat landslides (Tokat-Turkey), and susceptibility mapping by means of GIS. Env Geol 50(4):461–472

    Article  Google Scholar 

  • Yilmaz I, Yüksek AG (2008a) An example of artificial neural network application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795

    Article  Google Scholar 

  • Yilmaz I, Yüksek AG (2008b) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, ANFIS models and their comparison. Int J Rock Mech Min Sci 46(4):803–810

    Google Scholar 

  • Yilmaz I, Ekemen T, Yıldırım M, Keskin I, Özdemir G (2006) Failure and flow development of a collapse induced complex landslide: the 2005 Kuzulu (Koyulhisar-Turkey) landslide hazard. Env Geol 49(3):467–476

    Article  Google Scholar 

  • Zinko U, Seibert J, Dynesius M, Nilsson C (2005) Plant species numbers predicted by a topography based groundwater-flow index. Ecosystems 8:430–441

    Article  Google Scholar 

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Acknowledgments

Author thanks the two anonymous reviewers for their very constructive and valuable comments which significantly led to the improvement of the paper.

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  1. Department of Geological Engineering, Faculty of Engineering, Cumhuriyet University, 58140, Sivas, Turkey

    Işık Yilmaz

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Yilmaz, I. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61, 821–836 (2010). https://doi.org/10.1007/s12665-009-0394-9

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