SpringerLink
Log in

Landslide susceptibility map** using rough sets and back-propagation neural networks in the Three Gorges, China

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

Abstract

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility map** on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Akgun 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 

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

    Article  Google Scholar 

  • Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility map**: the Staffora river basin case study, Italy. Math Geosci 44:47–70

    Article  Google Scholar 

  • Bui DT, Lofman O, Revhaug I, Dick O (2011a) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444

    Article  Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2011b) Landslide susceptibility map** at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211

    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. doi:10.1155/2012/974638

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) 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

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33

    Article  Google Scholar 

  • Carro M, De Amicis M, Luzi L, Marzorati S (2003) The application of predictive modeling techniques to landslides induced by earthquakes, the case study of the 26 September 1997 Umbria–Marche earthquake (Italy). Eng Geol 69:139–159

    Article  Google Scholar 

  • Choi J, Oh HJ, Won JS, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility map**. Environ Earth Sci 60:473–483

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41(6):720–730

    Article  Google Scholar 

  • Falaschi F, Giacomelli F, Federici PR, D’Amato Avanzi G, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50:551–569

    Article  Google Scholar 

  • Fourniadis IG, Liu JG, Mason P (2007) Landslide hazard assessment in the Three Gorges area, China, using ASTER imagery: Wushan-Badong. Geomorphology 84:126–144

    Article  Google Scholar 

  • Gokceoglu C, Sonmez H, Ercanoglu M (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Eng Geol 55:277–296

    Article  Google Scholar 

  • Gorsevski PV, Piotr J (2008) Discerning landslide susceptibility using rough sets. Comput Environ Urban 32:53–65

    Article  Google Scholar 

  • Guzzetti F, Carrarra 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 

  • He SW, Pan P, Dai L, Wang HJ, Liu JP (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171–172:30–41

    Article  Google Scholar 

  • Hubei Province Geological Survey (1965) Geological map of Zigui-Wushan (1:200,000)

  • 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(3–4):347–366

    Article  Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in penialpine Slovenia. Geomorphology 74(1–4):17–28

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility map** using GIS and remote sensing data. Int J Remote Sens 26:1477–1491

    Article  Google Scholar 

  • Lee S (2007) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Proc Land 32:2133–2148

    Article  Google Scholar 

  • Liu JP, Zeng ZP, Liu HQ, Wang HB (2011) A rough set approach to analyze factors affecting landslide incidence. Comput Geosci 37:1311–1317

    Article  Google Scholar 

  • Oh JJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide susceptibility map** in a tropical hilly area. Comput Geosci 37(9):1264–1276

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  Google Scholar 

  • Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Pawlak Z (1997) Rough set approach to knowledge-based decision support. Eur J Oper Res 99(1):48–57

    Article  Google Scholar 

  • Pawlak Z, Slowinski R (1994) Rough set approach to multi-attribute decision analysis. Eur J Oper Res 72:443–459

    Article  Google Scholar 

  • Pradhan B (2010a) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Int Sys 3(3):370–381

    Google Scholar 

  • Pradhan B (2010b) Landslide susceptibility map** of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320

    Article  Google Scholar 

  • Pradhan B (2010c) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45(10):1244–1256

    Article  Google Scholar 

  • Pradhan B (2011a) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques to landslide susceptibility map** and their comparison with logistic regression modelling. Environ Ecol Stat 18(3):471–493

    Article  Google Scholar 

  • Pradhan B (2011b) 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 (2012) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility map** using GIS. Comput Geosci. http://dx.doi.org/10.1016/j.cageo

  • Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16(2):107–126

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:12–30

    Article  Google Scholar 

  • Pradhan B, Pirasteh P (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility map**. Disa Adv 3(2):26–34

    Google Scholar 

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

    Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility map**: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  • Shou KJ, Wang CF (2003) Analysis of the Chiufengershan landslide triggered by the 1999 Chi–Chi earthquake in Taiwan. Eng Geol 68:237–250

    Article  Google Scholar 

  • Süzen 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 

  • Süzen 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(3–4):303–321

    Article  Google Scholar 

  • Wu SR, Shi L, Wang RJ, Tan CX, Hu DG, Mei YT, Xu RC (2001) Zonation of the landslide hazards in the forereservoir region of the Three Gorges Project on the Yangtze River. Eng Geol 59:51–58

    Article  Google Scholar 

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility map** in Trabzon, NE Turkey. Catena 85(3):274–287

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility map** in Injae, Korea, using a decision tree. Eng Geol 116(3–4):274–283

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility map**: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266

    Article  Google Scholar 

  • Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility map** by artificial neural networks. Bull Eng Geol Environ 68:297–306

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zarea M, Pourghasemi HR, Vafakhah M, Pradhan B (2012) Landslide susceptibility map** at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. doi:10.1007/s12517-012-0610-x

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Prof. Pradhan and Dr. LaMoreaux for the helpful comments which improved the manuscript greatly. The study is jointly supported by NSFC (41271455/D0108), Open Research Fund of Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs (LDRERE20120207), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUGL120207), and Open Research Fund Program of Key Laboratory of Digital Map** and Land Information Application Engineering, National Administration of Surveying, Map** and Geoinformation (GCWD201101).

Author information

Authors and Affiliations

  1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Xueling Wu, Ruiqing Niu & Ling Peng

  2. School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, China

    Fu Ren

Authors
  1. Xueling Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruiqing Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fu Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ling Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruiqing Niu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, X., Niu, R., Ren, F. et al. Landslide susceptibility map** using rough sets and back-propagation neural networks in the Three Gorges, China. Environ Earth Sci 70, 1307–1318 (2013). https://doi.org/10.1007/s12665-013-2217-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-013-2217-2

Keywords

Search

Navigation