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Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea

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Abstract

The objective of this study was to produce and evaluate a landslide susceptibility map for weathered granite soils in Deokjeok-ri Creek, South Korea. The relative effect (RE) method was used to determine the relationship between landslide causative factors (CFs) and landslide occurrence. To determine the effect of CFs on landslides, data layers of aspect, elevation, slope, internal relief, curvature, distance to drainage, drainage density, stream power index, sediment transport index, topographic wetness index, soil drainage character, soil type, soil depth, forest type, timber age, and geology were analyzed in a geographical information system (GIS) environment. A GIS-based landslide inventory map of 748 landslide locations was prepared using data from previous reports, aerial photographic interpretation, and extensive field work. A RE model was generated from a training set consisting of 673 randomly selected landslides in the inventory map, with the remaining 75 landslides used for validation of the susceptibility map. The results of the analysis were verified using the landslide location data. According to the analysis, the RE model had a success rate of 86.3 % and a predictive accuracy of 88.6 %. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. The results of this study can therefore be used to mitigate landslide-induced hazards and to plan land use.

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References

  • Akgün A, Kıncal C, Pradhan B (2011) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess. doi:10.1007/s10661-011-2352-8 (Article first available online)

  • Akgün A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34

    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(1):73–81

    Article  Google Scholar 

  • Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Process Landf 26(12):251–1263

    Article  Google Scholar 

  • Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulas railway case study. Phys Chem Earth Parts A/B/C 35(3–5):162–171

    Article  Google Scholar 

  • Bonham-Carter GF (1991) Integration of geoscientific data using GIS. In: Goodchild MF, Rhind DW, Maguire DJ (eds) Geographic information systems: principle and applications. Longdom, London, pp 171–184

    Google Scholar 

  • Brabb EE (1984) Innovative approaches to landslide hazard mapping. In: Proceedings 4th international symposium on landslides, Toronto, vol 1, pp 307–324

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

    Article  Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and naïve Bayes models. Math Probl Eng 2012:1–26. doi:10.1155/2012/974638

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility assessment in the Hoa Binh province of Vietnam using artificial neural network. Geomorphology. doi:10.1016/j.geomorph.2012.04.023 (Article first available online)

  • Carrara A, Cardinali M, Guzzeti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer Academic, Dordrecht, pp 135–175

    Chapter  Google Scholar 

  • Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44:949–962

    Article  Google Scholar 

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

    Google Scholar 

  • Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Chung YS, Yoon MB, Kim HS (2004) On climate variations and changes observed in South Korea. Clim Change 66(1–2):151–161

    Article  Google Scholar 

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • Dhital MR, Shrestha R, Ghimire M, Shrestha GB, Tripathi D (2006) Hydrological hazard mapping in Rupandehi district, West Nepal. J Nepal Geol Soc 31:59–66

    Google Scholar 

  • Einstein HH (1988) Special lecture: landslides risk assessment procedure. In: Proceedings of 5th symposium on landslides, Lausanne, vol 2, pp 1075–1090

  • Ercanoglu M, Gokceoglu C, Van Aseh W (2004) Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–32

    Article  Google Scholar 

  • Garcia RAC, Zêzere JL and Oliveira SC (2008) The influence of terrain units in landslide susceptibility assessment: a case study in the Abadia Basin (Portugal). Geophys Res Abstr, vol 10, EGU2008-A-07486

  • Ghimire M (2001) Geo-hydrological hazard and risk zonation of Banganga watershed using GIS and remote sensing. J Nepal Geol Soc 23:99–110

    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 techniques. Eng Geol 44:147–161

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: an aid to a sustainable development. Geomorphology 31:181–216

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hengl T, Gruber S, Shrestha DP (2003) Digital terrain analysis in ILWIS. International Institute for Geo-Information Science and Earth Observation, Enschede

    Google Scholar 

  • Kim W, Kim K, Chae B, Cho Y (2000) Case study of landslide types in Korea. J Eng Geol 10(2):18–35

    Google Scholar 

  • Kim J, Jeong S, Park S, Sharma J (2004) Influence of rainfall induced wetting on the stability of slopes in weathered soils. Eng Geol 75(3–4):251–262

    Article  Google Scholar 

  • Kwon Y, Oh S (2011) Physical and mechanical properties of decomposed granite soils sampled in Cheongju, Korea. Int J Phys Sci 6(24):5777–5794

    Google Scholar 

  • Lee SG, de Freitas MH (1989) A revision of the description and classification of weathered granite and its application to granites in Korea. Eng Geol 22(1):31–48

    Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia, using frequency ratio and logistic regression models. Landslides 4(1):33–41

    Article  Google Scholar 

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

    Google Scholar 

  • Luzi L, Pergalani F (1999) Slope instability in static and dynamic conditions for urban planning: the “Oltre Po Pavese” case history (Region Lombardia, Italy). Nat Hazards 20:57–82

    Article  Google Scholar 

  • McCalpin J (1974) Preliminary age classification of landslides for inventory mapping: 21st annual symposium on engineering geology and soils engineering. Proceedings, University of Idaho, Moscow, Idaho, USA, pp 99–111

  • Mejia-Navarro M, Garcia LA (1996) Natural hazard and risk assessment using decision support systems, application: Glenwood Springs, Colorado. Environ Eng Geosci 2(3):299–324

    Google Scholar 

  • Mejia-Navarro M, Wohl EE (1994) Geological hazard and risk evaluation using GIS: methodology and model applied to Medellin, Colombia. Bull As Eng Geol 31:459–481

    Google Scholar 

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

  • Moore I, Burch G (1986) Physical basis of the length–slope factor in the universal soil loss equation. Soil Soc Am J 50:1294–1298

    Article  Google Scholar 

  • Moore ID, Wilson JP (1992) Length–slope factors for the revised universal soil loss equation: simplified method of estimation. J Soil Water Conserv 47:423–428

    Google Scholar 

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

    Article  Google Scholar 

  • Nagarajan R, Roy A, Kumar RV, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Environ 58:275–287

    Article  Google Scholar 

  • Neelkantan R, Yuvaraj S (2012) Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India. Arab J Geosci. doi:10.1007/s12517-012-0693-4

    Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 7(9):1264–1276. doi:10.1016/j.cageo.2010.10.012

    Article  Google Scholar 

  • Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36(3–4):325–334

    Article  Google Scholar 

  • Park S, Choi C, Kim B (2012) 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. doi:10.1007/s12665-012-1842-5

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84. doi:10.1016/j.catena.2012.05.005

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2012c) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz watershed, Iran. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp. 23–49. doi:10.1007/978-3-642-25495-6-2

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012c) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. doi:10.1007/s11069-012-0217-2

  • Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320. doi:10.1007/s12524-010-0020-z

    Article  Google Scholar 

  • Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349

    Article  Google Scholar 

  • Pradhan B, Lee S (2009) Landslide risk analysis using an artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15

    Google Scholar 

  • Pradhan B, Lee S (2010a) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pradhan B, Lee S (2010c) Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  • Pradhan B, Singh RP, Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709

    Article  Google Scholar 

  • Pradhan B, Lee S, Mansor S, Buchroithner MF, Jallaluddin N, Khujaimah Z (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using a binomial logistic regression model. Appl Remote Sens 2:1–11

    Google Scholar 

  • Radbruch DH (1970) Map of relative amounts of landslides in California. US Geol Surv Open-File Rep 70-1485, p 36, map scale 1:500.000. US Geol Surv Open-File Rep, pp 85–585

  • Rahardjo H, Lee T, Leong EC, Rezaur RB (2005) Response of a residual soil slope to rainfall. Can Geotech J 42(2):340–351

    Article  Google Scholar 

  • 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 109:108–121

    Article  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(7):8208–8219

    Article  Google Scholar 

  • Sidle RC, Pearce AJ, Loughlin CLO (1985) Hillslope stability and land-use. American Geophysical Union, Washington, p 125

    Book  Google Scholar 

  • Soeters R, Westen CJ (1996) Slope instability recognition, analysis, and zonation. In: Turner AK, Schuster RL (eds) Landslides, investigation and mitigation. National Academy Press, Washington. ISBN 0-309-06151-2 (Transportation Research Board, National Research Council, Special Report; 247), pp 129–177

  • Soil Survey Staff (1993) Soil survey manual. Soil Conservation Service. US Department of Agriculture Handbook 18. Archived from the original on 2006-02-14. Retrieved 2006-07-02

  • Stocking MA (1972) Relief analysis and soil erosion in Rhodesia using multivariate techniques. Z Geomorphol 16:432–443

    Google Scholar 

  • Tunusluoglu MC, Gokceoglu C, Nefeslioglu HA, Sonmez H (2008) Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environ Geol 54:9–22

    Article  Google Scholar 

  • United States Department of Agriculture (USDA) (1993) Soil survey manual. Soil Survey Division Staff, National Soil Survey Center, Washington

    Google Scholar 

  • Upreti BN, Dhital MR (1996) Landslide studies and management in Nepal. International Centre for Integrated Mountain Development (ICMOD), Kathmandu

    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(9):1101–1114

    Article  Google Scholar 

  • Van Westen CJ (1994) GIS in landslide hazard zonation: a review, with examples from the Andes of Colombia. In: Price MF, Heywood DI (eds) Mountain environments and geographic information systems. Taylor and Francis, The Netherlands, pp 135–165

    Google Scholar 

  • Van Westen C (1997) Statistical landslide hazard analysis ILWIS 2.1 for Windows application guide. ITC, Enschede, pp 73–84

    Google Scholar 

  • Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  • Varnes DJ (1984) Commission on landslides and other mass movements: landslide hazard zonation: a review of principles and practice. UNESCO Press, Paris

    Google Scholar 

  • Vázquez-Selem L, Zinck AJ (1994) Modeling gully distribution on volcanic terrains in the Huasca area, central Mexico. ITC J 3:238–251

    Google Scholar 

  • Wan S (2009) A spatial decision support system for extracting the core factors and thresholds for a landslide susceptibility map. Eng Geol 108:237–251

    Article  Google Scholar 

  • Wieczorek GF (1984) Preparing a detailed landslide-inventory map for hazard evaluation and reduction. As Eng Geol Bull 21(3):337–342

    Google Scholar 

  • Yalcin A (2005) An investigation of the Ardesen (Rize) region based on landslide susceptibility. Karadeniz Technical University, PhD Thesis (in Turkish)

  • Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Inje, Korea, using a decision tree. Eng Geol 116(2010):274–283

    Article  Google Scholar 

  • Zinck JA, López J, Metternicht GI, Shrestha DP, Vázquez-Selem L (2001) Mapping and modelling mass movements and gullies in mountainous areas using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinf 3(1):43–53

    Article  Google Scholar 

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Acknowledgments

The authors are thankful to anonymous reviewers for their valuable comments that were very useful in bringing the manuscript into its present form. Dr. Saro Lee, Principal Researcher in KIGAM, Mr. Hyo-Sub Kang, and Mr. Ji-Sung Lee are sincerely acknowledged for their great help during the field work and in writing this manuscript. This research was supported by the Public Welfare and Safety Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning (Grant No. 2012M3A2A1050977) and the Brain Korea 21 Plus (BK21Plus).

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Pradhan, A.M.S., Kim, YT. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Nat Hazards 72, 1189–1217 (2014). https://doi.org/10.1007/s11069-014-1065-z

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