Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China

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

Abstract

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

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 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. B Eng Geol Environ 58(1):21–44

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 43:3–31

    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. 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: 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 

  • 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 

  • Cho JH, Kurup PU (2011) Decision tree approach for classification and dimensionality reduction of electronic nose data. Sensor Actuat B 160(1):542–548

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Crist EP, Laurin R, Cicone RC (1986) Vegetation and soils information contained in transformed thematic mapper data. In: Proceedings of IGARSS symposium, Zurich, 1986

  • Definiens 2007 Developer 8: userguide. Definiens Imaging GmbH

  • Deng QL, Zhu ZY, Cui ZQ, Wang XP (2000) Mass rock creep and landsliding on the Huangtupo slope in the reservoir area of the Three Gorges project, Yangtze River, China. Eng Geol 58:67–83

    Article  Google Scholar 

  • Erener A, Duzgun HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Environ Earth Sci 66:859–877

    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, Jankowski P (2008) Discerning landslide susceptibility using rough sets. Comput Environ Urban 32:53–65

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184

    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)

  • Liu JG, Mason PJ, Clerici N, Chen S, Davis A, Miao F, Deng H, Liang L (2004) Landslide hazard assessment in the Three Gorges area of theYangtze river using ASTER imagery: Zigui–Badong. Geomorphology 61:171–187

    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 

  • Marjanovic M, Kovacevic M, Bajat B, Vozenılek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234

    Article  Google Scholar 

  • Miller HJ, Han J (2001) Geographic data mining and knowledge discovery: An overview. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Taylor and Francis, London, pp 3–32

    Chapter  Google Scholar 

  • Murthy SK (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Disc 2(4):345–389

    Article  Google Scholar 

  • Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemometr 18(6):275–285

    Article  Google Scholar 

  • Nandi A, Shakoor A (2008) Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed, Ohio. USA. Georisk 2(1):16–27

    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 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed Iran. Nat Hazards 63(2):965–996

    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

    Article  Google Scholar 

  • Pradhan B (2010b) 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

    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 mapping 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 (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • 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 (2010a) Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Modell Softw 25:747–759

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) 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 mapping. Disa Adv 3(2):26–34

    Google Scholar 

  • Pradhan B, Youssef AM, Varathrajoo R (2010a) Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-Spatial Inf Sci 13(2):93–102

    Article  Google Scholar 

  • Pradhan B, Oh HJ, Buchroithner M (2010b) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1(3):199–223

    Article  Google Scholar 

  • Pradhan B, Mansor S, Pirasteh S, Buchroithner MF (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32(14):4075–4087

    Article  Google Scholar 

  • Richards JA (1999) Remote sensing digital image analysis: an introduction. Springer-Verlag, Berlin

    Book  Google Scholar 

  • Rotigliano E, Cappadonia C, Conoscenti C, Costanzo D, Agnesi V (2012) Slope units-based flow susceptibility model: using validation tests to select controlling factors. Nat Hazards 61:143–153

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sorensen K, Janssens GK (2003) Data mining with genetic algorithms on binary trees. Eur J Oper Res 151:253–264

    Article  Google Scholar 

  • Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using random forests. Remote Sens Environ 115:2564–2577

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Wang XM, Niu RQ (2010) Landslide intelligent prediction using object-oriented method. Soil Dyn Earthq Eng 30:1478–1486

    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 

  • 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 

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

    Article  Google Scholar 

  • Zarea M, Pourghasemi HR, Vafakhah M, Pradhan B (2012) Landslide susceptibility mapping 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

Thanks to Dr. LaMoreaux and three anonymous reviewers for their valuable comments. This study is jointly supported by NSFC (41271455/D0108), Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUGL120207).

Author information

Authors and Affiliations

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

    Xueling Wu & Ruiqing Niu

  2. School of Resource and Environmental Sciences, 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. Fu Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruiqing Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fu Ren.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, X., Ren, F. & Niu, R. Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environ Earth Sci 71, 4725–4738 (2014). https://doi.org/10.1007/s12665-013-2863-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-013-2863-4

Keywords

Search

Navigation