Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).

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

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. Landslide 9:93–106

    Article  Google Scholar 

  • Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ration and weighted linear combination models. Environ Geol 54:1127–1143

    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 

  • 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 

  • Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Proc Land 26(12):1251–1263

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists, modeling with GIS. Pergamon Press, Oxford, p 398

    Google Scholar 

  • Cardinali M, Reichenbach R, 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 Sys Sci 2:57–72

    Article  Google Scholar 

  • Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15(3):403–426

    Article  Google Scholar 

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

  • Castellanos Abella EA, Van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslides 4:311–325

    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 

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

    Article  Google Scholar 

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

    Google Scholar 

  • Chung CJF, Fabbri A (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazard 30:451–472

    Article  Google Scholar 

  • Chung CF, Fabbri AG, Van Westen CJ (1995) Multivariate regression analysis for landslide hazard zonalition. In: Carrara A, Guzetti F (eds) Geographical informations systems in assessing natural hazards. Kluwer Publishers, Dordrecht, pp 107–133

    Google Scholar 

  • CRED (2009) Centre for Research on the Epidemiology of Disasters (CREM) website. http://www.dmdat.be/

  • 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 

  • Dietrich EW, Reiss R, Hsu ML, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrol Process 9:383–400

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Erdas (2011) Intergraph corporate website, http://www.erdas.com/

  • Erener A, Düzgün HSB (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68

    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 

  • Garcia-Rodriguez MJ, Malpica JA, Benito B, Diaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95:172–191

    Article  Google Scholar 

  • Garrett J (1994) Where and shy artificial neural networks are applicable in civil engineering. J Comput Civil Eng 8:129–130

    Article  Google Scholar 

  • Godt JW, Baum RL, Savage WZ, Salciarini D, Schulz WH, Harp EL (2008) Transient deterministic shallow landslide modeling : Requirements for susceptibility and hazard assessment in a GIS framework. Eng Geol 102:214–226

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jadda M, Shafri HZM, Mansor SB, Sharifikia M, Pirasteh S (2009) Landslide susceptibility evaluation and factor effect analysis using probabilistic-frequency ratio model. Eur J Sci Res 33:654–668

    Google Scholar 

  • Jin CG, Oh CY, Choi CU (2010) The comparative research of landslide susceptibility mapping. In: Proceedings of ESRI Education User Conference 2010

  • 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 

  • Kavzoglu T (2001) An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images. Ph.D. dissertation, University of Nottingham, School of Geography, UK, p 306

  • Korea Forest Service (2006) Korea Forest Service website. http://sansatai.forest.go.kr/

  • Korea Meteorological Administration (2006) Korea Meteorological Administration website. http://www.kma.go.kr/

  • Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52(4):615–623

    Article  Google Scholar 

  • Lee HW (2011) Analysis of landslide susceptibility using probabilistic method and GIS. Sejong university, mater’s thesis (in Korean)

  • 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(6):778–787

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    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 50:847–855

    Article  Google Scholar 

  • Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47(7):982–990

    Article  Google Scholar 

  • Lee CJ, Yoo NJ (2009) A study on debris flow landslide disasters and restoration at Inje of Kangwon Province, Korea. J Korean Soc Hazard Mitig 9(1):99–105 (in Korean)

    Google Scholar 

  • Lee S, Kim YJ, Min KD (2000) Development of spatial landslide information system and application of spatial landslide information. J GIS assoc Korea 8:141–153 (in Korean)

    Google Scholar 

  • Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol 44:820–833

    Article  Google Scholar 

  • Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models : case study of Youngin, Korea. Landslides 4(4):327–338

    Article  Google Scholar 

  • Lim Khai-Wern K, Lea Tien T, Lateh H (2011) Landslide hazard mapping of Penang Island using probabilistic methods and logistic regression. Imaging System and Techniques (IST), 2011 IEEE International Conference on, pp 273–278

  • 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

  • Manel S, Dias JM, Ormerod SJ (1999) Comparing discriminant analysis, neural networks and logistic regression for predicting species’ distributions: a case study with a Himalayan river bird. Ecol Model 120:337–347

    Article  Google Scholar 

  • McFadden D (1973) Conditional logit analysis of quantitative choice behavior. In: Zarembka P (ed) Frontiers in Econometrics. Academic Press, New York, pp 105–142

    Google Scholar 

  • Ministry of Land, Transport and Maritime Affairs (2006) Investigation on the typhoon and heavy rainfall, p 497 (in Korean)

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

    Article  Google Scholar 

  • Naranjo JL, van Western CJ, Soeters R (1994) Evaluating the use of training areas in bivariate statistical landslide hazard analysis: a case study in Colombia. J Int Inst Aerospace Surv Earth Sci 3:292–300

    Google Scholar 

  • National Emergency Management Agency (2009) Development of landslide prediction technology and damage mitigation countermeasures, pp 41, 114–116 (in Korean)

  • Neuhäuser B, Damm B, Terhorst B (2011) GIS-based assessment of landslide susceptibility on the base of the weights-of-evidence model. Landslides. doi:10.1007/s10346-011-0305-5

    Google Scholar 

  • Oh H, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37:1264–1276

    Article  Google Scholar 

  • Oh CY, Jin CG, Choi CU (2010) The comparative research of landslide susceptibility mapping using FR, AHP, LR, ANN. In: Proceedings of The Korean Society for Geo-Spatial Information System Conference 2010 (in Korean)

  • Park DG, Kim TH, Oh JL, Han TG (2005) Improvement of Countermeasures for Slope Failure Mitigation in Korea. Proc Korean Geotech Soc Confer 103:107–116 (in Korean)

    Google Scholar 

  • Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002) Using neural networks and GIS to forecast land use changes : a Land Transformation Model. Comput Environ Urban 26:552–575

    Article  Google Scholar 

  • Pradhan B (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010a) 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 

  • Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010b) Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177

    Article  Google Scholar 

  • Rumelhart D, Hinton G, Williams R (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructures of cognition. MIT Press, Cambridge, pp 318–362

    Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281

    Article  Google Scholar 

  • Saaty TL, Vargas LG (1991) Prediction, projection and forecasting: applications of the analytic hierarchy process in economics, finance, politics, games, and sports. Kluwer Academic Publishers, Boston, p 251p

    Book  Google Scholar 

  • Schneider L, Pontius RG Jr (2001) Modeling land-use change: the case of the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85:83–94

    Article  Google Scholar 

  • Schuster R (1996) Socioeconomic significance of landslides. In: Turner AK, Schuster RL (eds) Landslides : investigation and mitigation, special report, vol 247. National Academic PressWashington, DC, pp 12–36

    Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219

    Article  Google Scholar 

  • Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis and zonation. In: AK Turner, RL Schuster (eds) Landslides investigation and mitigation. Transportation Research Board, spec rep 247, National Academy Press, Washington, pp 129–177

  • Statistics Korea (2009) Gangwon-Do annual statistic report (in Korean)

  • Suzen ML, Doyuran V (2004a) A comparison of the GIS based landslide susceptibility assessment methods multivariate versus bivariate. Environ Geol 45(5):665–679

    Article  Google Scholar 

  • Suzen ML, Doyuran V (2004b) 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 

  • Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51:439–450

    Article  Google Scholar 

  • Thiery Y, Malet JP, Sterlacchini S, Puissant A, Maquaire O (2008) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92:38–59

    Article  Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36:1101–1114

    Article  Google Scholar 

  • Van Westen CJ, Terlien MJ (1996) An approach towards deterministic landslide hazard analysis in GIS, A case study from Manizales (Colombia). Earth Surf Proc Land 21(9):853–868

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wu F (2002) Calibration of stochastic cellular automata: The application to rural-urban land conversions. Int J Geogr Inf Sci 16(8):795–818

    Article  Google Scholar 

  • Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Tukey): Comparisons of results and confirmations. Catena 71:1–12

    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 35:1125–1138

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yoshimatsu H, Abe S (2006) A review of landslide hazards in Japan and assessment of their susceptibility using an analytical process (AHP) method. Landslides 3:149–158

    Article  Google Scholar 

  • Zurada JM (1992) Introduction to artificial neural systems, Wet Pub. Co., pp 163–248

Download references

Acknowledgments

This work was researched by the supporting project to educate GIS experts. Thanks are also extended to two anonymous reviewers who suggested some improvements to the manuscript.

Author information

Authors and Affiliations

  1. Department of Spatial Information Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 608-737, Republic of Korea

    Soyoung Park, Chuluong Choi & Byungwoo Kim

  2. ZEN21, 2nd Floor, RNC Building, 981-1 Bangbae 3-dong, Seocho-gu, Seoul, 137-848, Republic of Korea

    Jinsoo Kim

Authors
  1. Soyoung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuluong Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Byungwoo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinsoo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinsoo Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, S., Choi, C., Kim, B. et al. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68, 1443–1464 (2013). https://doi.org/10.1007/s12665-012-1842-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-012-1842-5

Keywords

Search

Navigation