Skip to main content
Log in

GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China

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

Abstract

Landslides have had a huge effect on human life, the environment and local economic development, and therefore they need to be well understood. In this study, we presented an approach for the analysis and modeling of landslide data using rare events logistic regression and applied the approach to an area in Lianyungang, China. Digital orthophotomaps, digital elevation models of the region, geological maps and different GIS layers including settlement, road net and rivers were collected and applied in the analysis. Landslides were identified by monoscopic manual interpretation and validated during the field investigation. To validate the quality of mapping, the data from the study area were divided into a training set and validation set. The result map showed that 4.26% of the study area was identified as having very high susceptibility to landslides, whereas the others were classified as having very low susceptibility (47.2%), low susceptibility (22.21%), medium susceptibility (14.39%) and high susceptibility (11.93%). The quality of the landslide-susceptibility map produced in this paper was validated, and it can be used for planning protective and mitigation measures. The landslide-susceptibility map is a fundamental part of the Lianyungang city landslide risk assessment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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, Bulut F (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ Geol 51:1377–1387

    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 

  • Allison PD (2001) Logistic regression using the SAS system: theory and application. Wiley Interscience, New York, p 288

    Google Scholar 

  • Atkinson PM, Massari R (1998) Generalized linear modeling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24:373–385

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2009) GIS-based and data-driven bivariate landslide-susceptibility mapping in the Three Gorges area, China. Pedosphere 19(1):14–20

    Article  Google Scholar 

  • Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31

    Article  Google Scholar 

  • Bell R, Glade T (2004) Quantitative risk analysis for landslides—examples from Bdudalur, NW-Iceland. Nat Hazard Earth Syst Sci 4(1):117–131

    Article  Google Scholar 

  • Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses. Geomorphology 72:250–271

    Article  Google Scholar 

  • Chang K-T, Chiang S-H, Hsu M-L (2007) Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology 89:335–347

    Article  Google Scholar 

  • Chung CF, Kojima H, Fabbri AG (2002) Stability analysis of prediction models for landslide hazard mapping. In: Allison RJ (ed) Applied geomorphology: theory and practice. Wiley, London, pp 1–19

    Google Scholar 

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Shuster RL (eds) Landslides: investigation and mitigation. Transp Res Board, Special Report 247, pp 36–75

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS Lantau Island, Hong Kong. Geomorphology 42:213–238

    Article  Google Scholar 

  • Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391

    Article  Google Scholar 

  • Domínguez-Cuesta MJ, Jiménez-Sánchez M, Berrezueta E (2007) Landslides in the central coalfield (Cantabrian Mountains, NW Spain): geomorphological features, conditioning factors and methodological implications in susceptibility assessment. Geomorphology 89:358–369

    Article  Google Scholar 

  • Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51:241–256

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Glade T (2005) Linking debris-flow hazard assessments with geomorphology. Geomorphology 66:189–213

    Article  Google Scholar 

  • Greco R, Sorriso-Valvo M, Catalano E (2006) Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study, Calabria, Italy. Eng Geol 89:47–66

    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 

  • Guzzetti P, Reichenbach M, Cardinali M, Ardizzone GalliF (2005) Landslide hazard assessment in the Staffora basin, northern Italian Apennines. Geomorphology 72:272–299

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (1989) Applied Regression Analysis. Wiley, New York, p 307

    Google Scholar 

  • Imai K, King G, Lau OK (2005) Zelig: everyone’s statistical software, Version 2.1-3, User’s manual. 197 pp. (http://gking.harvard.edu/zelig/docs/zelig.pdf)

  • Keefer DK (1984) Landslides caused by earthquakes. Bull Geol Soc Am 95:406–421

    Article  Google Scholar 

  • King G, Zeng L (2001) Logistic regression in rare events data. Political Anal 9:37–163

    Google Scholar 

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

    Article  Google Scholar 

  • Menard SW (1995) Applied logistic regression analysis. SAGE Publication, Inc, Thousand Oaks

    Google Scholar 

  • Moore ID, Burch GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resour Res 22:1350–1360

    Article  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 

  • 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 97:171–191

    Article  Google Scholar 

  • Ohlmacher CG, Davis CJ (2003) Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343

    Article  Google Scholar 

  • Soeters R, van Westen CJ (1996) Slope instability recognition analysis and zonation. In: Turner KT, Schuster RL (eds) Landslides: investigation and mitigation. Transportation Research Board National Research Council, Special Report, Washington, DC, pp 129–177

    Google Scholar 

  • Süzen ML, Doyuran V (2004a) 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 

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

    Article  Google Scholar 

  • Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Van Westen CJ, Castellanos Enrique, Kuriakose SekharL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides, analysis and control. Transportation Research Board Sp. Rep. No. 176, National Academy of Sciences, Philadelphia, pp 11–33

    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 

Download references

Acknowledgments

This study was supported by the National Natural Science Foundation of China (nos. 40801212 and 40871010), the National Natural Science Foundation of China (Key Project) (no. 40730527), the National Key Basic Research Program of the early special issues (no. 2007CB416602) and the Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection of Chendu University of Technology, China (no. GZ2007-11).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shibiao Bai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bai, S., Lü, G., Wang, J. et al. GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62, 139–149 (2011). https://doi.org/10.1007/s12665-010-0509-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-010-0509-3

Keywords

Navigation