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Landslides

, Volume 13, Issue 2, pp 361–378 | Cite as

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

  • Dieu Tien BuiEmail author
  • Tran Anh Tuan
  • Harald Klempe
  • Biswajeet Pradhan
  • Inge Revhaug
Original Paper

Abstract

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

Keywords

Landslide GIS Support vector machines Neural network Kernel logistic regression Decision trees Son La hydropower 

Notes

Acknowledgment

This research was supported by the Geographic Information System group, Department of Business Administration and Computer Science, Faculty of Art and Sciences, Telemark University College, Bø i Telemark, Norway. The authors would like to thank Professor Candan Gokceoglu and three anonymous reviewers for their valuable and constructive comments on the earlier version of the manuscript.

References

  1. Abe S (2010) Support vector machines for pattern classification. Springer, LondonCrossRefGoogle Scholar
  2. Aguirre-Gutiérrez J, Carvalheiro LG, Polce C, van Loon EE, Raes N, Reemer M, Biesmeijer JC (2013) Fit-for-purpose: species distribution model performance depends on evaluation criteria—Dutch hoverflies as a case study. PLoS One 8:e63708CrossRefGoogle Scholar
  3. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106CrossRefGoogle Scholar
  4. 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 38:23–34CrossRefGoogle Scholar
  5. Allison PD (1999) Logistic regression using the SAS system: theory and application. SAS Institute, Inc., CaryGoogle Scholar
  6. Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24:373–385CrossRefGoogle Scholar
  7. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, central Japan. Geomorphology 65:15–31CrossRefGoogle Scholar
  8. Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Math Geosci 44:47–70CrossRefGoogle Scholar
  9. Beasley TM, Zumbo BD (2003) Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs. Comput Stat Data Anal 42:569–593CrossRefGoogle Scholar
  10. Belsley D (1991) A guide to using the collinearity diagnostics. Comput Sci Econ Manag 4:33–50Google Scholar
  11. Booth GD, Niccolucci MJ, Schuster EG (1994) Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. US Dept of Agriculture Forest Service, OgdenGoogle Scholar
  12. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30:1145–1159CrossRefGoogle Scholar
  13. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, BelmontGoogle Scholar
  14. Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862CrossRefGoogle Scholar
  15. Carrara A, Pike RJ (2008) GIS technology and models for assessing landslide hazard and risk. Geomorphology 94:257–260CrossRefGoogle Scholar
  16. Cawley G, Talbot NC (2008) Efficient approximate leave-one-out cross-validation for kernel logistic regression. Mach Learn 71:243–264CrossRefGoogle Scholar
  17. Chacon J, Irigaray C, Fernandez T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65:341–411CrossRefGoogle Scholar
  18. Chung C-J, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452CrossRefGoogle Scholar
  19. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  20. Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J (2012) Factors selection in landslide susceptibility modelling on large scale following the GIS matrix method: application to the River Beiro Basin (Spain). Nat Hazards Earth Syst Sci 12:327–340CrossRefGoogle Scholar
  21. Costanzo D, Chacón J, Conoscenti C, Irigaray C, Rotigliano E (2014) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides 11:639–653CrossRefGoogle Scholar
  22. Cross M (2002) Landslide susceptibility mapping using the matrix assessment approach: a Derbyshire case study. In: Griffiths JS (ed) Mapping in engineering geology, The Geological Society. Key Issue in Earth Sciences, London, pp 247–261Google Scholar
  23. Dan NT, Tuan TA, Thu TH, Hong PV, Hung LQ, Luong NV, Hai NT, Nhung H, Ha NTV, Thu DH, Thanh LV, Hien D, Mai D (2011) Application of remote sensing, GIS, and GPS for the study of landslides at the Son La hydropower basin and proposed remedial measures. Institute of Marine Geology & Geophysics, Vietnam Academy of Science and Technology, Hanoi, p 140Google Scholar
  24. D’Arco M, Liccardo A, Pasquino N (2012) ANOVA-based approach for DAC diagnostics. IEEE Trans Instrum Meas 61:1874–1882CrossRefGoogle Scholar
  25. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evol Comput 1:3–18CrossRefGoogle Scholar
  26. Do T, Bui Minh T, Truong Minh T, Trinh Xuan H, Nguyen Phuong M (2000) The investigation and assessment of environmental geology for the Son La hydropower basin and its surrounding areas. Ministry of Science, Technology and Environment of Vietnam, Hanoi, p 231Google Scholar
  27. Doetsch P, Buck C, Golik P, Hoppe N, Kramp M, Laudenberg J, Oberdörfer C, Steingrube P, Forster J and Mauser A (2009) Logistic model trees with AUC split criterion for the KDD Cup 2009 Small ChallengeGoogle Scholar
  28. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46CrossRefGoogle Scholar
  29. Dovzhikov AE, Mi BP, Vasilevskaya ED, Zhamoida AI, Ivanov GV, Izokh EP, Huu LD, Mareichev AM, Tien NV, Tri NT, Luong TD, Kuang PV, Long PD (1965) Geology of northern Vietnam. Science and Technology, Hanoi, p 668Google Scholar
  30. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191–209CrossRefGoogle Scholar
  31. Duc D (2013) Rainfall-triggered large landslides on 15 December 2005 in Van Canh district, Binh Dinh province, Vietnam. Landslides 10:219–230CrossRefGoogle Scholar
  32. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730CrossRefGoogle Scholar
  33. Erener A, Düzgün H (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68CrossRefGoogle Scholar
  34. Felicisimo A, Cuartero A, Remondo J, Quiros E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189CrossRefGoogle Scholar
  35. Fernández T, Irigaray C, El Hamdouni R, Chacón J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa Area (Granada, Spain). Nat Hazards 30:297–308CrossRefGoogle Scholar
  36. Forest Inventory and Planning Institute (2005) The forest map of Vietnam scale 1:50 000. Vietnam Forest Inventory and Planning Institute, HanoiGoogle Scholar
  37. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675–701CrossRefGoogle Scholar
  38. Gama J (2004) Functional trees. Mach Learn 55:219–250CrossRefGoogle Scholar
  39. General Department of Geology and Minerals of Vietnam (2000) Geological and mineral resources maps scale of 1:200,000Google Scholar
  40. Gil D and Johnsson M (2010) Supervised SOM based architecture versus multilayer perceptron and RBF networks. Proc Linköping Electron Conf: 15-24Google Scholar
  41. 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 44:147–161CrossRefGoogle Scholar
  42. Gokceoglu C, Sezer E (2009) A statistical assessment on international landslide literature (1945–2008). Landslides 6:345–351CrossRefGoogle Scholar
  43. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27CrossRefGoogle Scholar
  44. Guzzetti F, Galli M, Reichenbach P, Ardizzone F, Cardinali M (2006a) Landslide hazard assessment in the Collazzone area, Umbria, central Italy. Nat Hazards Earth Syst Sci 6:115–131CrossRefGoogle Scholar
  45. Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006b) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184CrossRefGoogle Scholar
  46. Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis. Prentice Hall, New YorkGoogle Scholar
  47. Haykin S (1998) Neural networks: a comprehensive foundation (2nd edition). Prentice Hall, Upper Saddle RiverGoogle Scholar
  48. Hungr O, Fell R, Couture R, Eberhardt E (2005) Landslide risk management. CRC PressGoogle Scholar
  49. Hunter E, Matin J, Stone P (1966) Experiments in induction. Academic, New YorkGoogle Scholar
  50. Irigaray C, Fernández T, El Hamdouni R, Chacón J (2007) Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards 41:61–79CrossRefGoogle Scholar
  51. Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165CrossRefGoogle Scholar
  52. Jia N, Mitani Y, Xie MW, Djamaluddin I (2012) Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area. Comput Geotech 45:1–10CrossRefGoogle Scholar
  53. Jiménez-Perálvarez JD, Irigaray C, El Hamdouni R, Chacón J (2011) Landslide susceptibility mapping in a semi-arid mountain environment: an example from the southern slopes of Sierra Nevada (Granada, Spain). Bull Eng Geol Environ 70:265–277CrossRefGoogle Scholar
  54. Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinform 11:352–359CrossRefGoogle Scholar
  55. Kavzoglu T, Mather PM (2003) The use of backpropagating artificial neural networks in land cover classification. Int J Remote Sens 24:4907–4938CrossRefGoogle Scholar
  56. Kavzoglu T, Kutlug Sahin E, Colkesen I (2014a) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat Hazards 1-26Google Scholar
  57. Kavzoglu T, Sahin E, Colkesen I (2014b) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439CrossRefGoogle Scholar
  58. Keith TZ (2006) Multiple regressions and beyond. Pearson, BostonGoogle Scholar
  59. Kononenko I (1994) Estimating attributes: analysis and extensions of relief. In: Bergadano F, Raedt L (eds) Machine learning: ECML-94. Springer, Berlin, pp 171–182CrossRefGoogle Scholar
  60. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  61. Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59:161–205CrossRefGoogle Scholar
  62. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26:1477–1491CrossRefGoogle Scholar
  63. Lee S (2007) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623CrossRefGoogle Scholar
  64. Lee S, Ryu JH, Min KD, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Process Landf 28:1361–1376CrossRefGoogle Scholar
  65. Liao D, Valliant R (2012) Variance inflation factors in the analysis of complex survey data. Surv Methodol 38:53–62Google Scholar
  66. Martínez-Álvarez F, Reyes J, Morales-Esteban A, Rubio-Escudero C (2013) Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula. Knowl-Based Syst 50:198–210CrossRefGoogle Scholar
  67. Mercer J (1909) Functions of positive and negative type, and their connection with the theory of integral equations. Philos Trans R Soc Lond Ser A, Containing Pap Math Phys Charact 209:415–446CrossRefGoogle Scholar
  68. Montgomery DR, Dietrich WE (1994) A physically-based model for the topographic control on shallow landsliding. Water Resour Res 30:1153–1171CrossRefGoogle Scholar
  69. Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl EngGoogle Scholar
  70. Pavel M, Fannin RJ, Nelson JD (2008) Replication of a terrain stability mapping using an artificial neural network. Geomorphology 97:356–373CrossRefGoogle Scholar
  71. Pourghasemi H, Pradhan B, Gokceoglu C (2012a) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996CrossRefGoogle Scholar
  72. Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2012b) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk 4:93–118CrossRefGoogle Scholar
  73. Pradhan B (2011) Manifestation of an advanced fuzzy logic model coupled with geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18:471–493CrossRefGoogle Scholar
  74. Pradhan B (2012) 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 51:350–365CrossRefGoogle Scholar
  75. Pradhan B, Lee S (2010a) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759CrossRefGoogle Scholar
  76. Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30CrossRefGoogle Scholar
  77. 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 34:216–235CrossRefGoogle Scholar
  78. 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 48:4164–4177CrossRefGoogle Scholar
  79. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo, CA, USAGoogle Scholar
  80. 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 109:108–121CrossRefGoogle Scholar
  81. Sasikala S, AppavualiasBalamurugan S, Geetha S (2014) Multi filtration feature selection (MFFS) to improve discriminatory ability in clinical data set. Appl Comput Inform. doi: 10.1016/j.aci.2014.03.002 Google Scholar
  82. Schuerman J (1983) Principal components analysis. Multivariate analysis in the human services. Springer, Netherlands, pp 93–119Google Scholar
  83. Şenkal O, Kuleli T (2009) Estimation of solar radiation over turkey using artificial neural network and satellite data. Appl Energy 86:1222–1228CrossRefGoogle Scholar
  84. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219CrossRefGoogle Scholar
  85. Sossa H, Guevara E (2014) Efficient training for dendrite morphological neural networks. Neurocomputing 131:132–142CrossRefGoogle Scholar
  86. Thanh L, De Smedt F (2014) Slope stability analysis using a physically based model: a case study from a Luoi district in Thua Thien-Hue province, Vietnam. Landslides 11:897–907CrossRefGoogle Scholar
  87. Tien Bui D (2012) Modeling of rainfall-induced landslide hazard for the hoa binh province of vietnam. Norwegian University of Life Sciences. Ph.D Thesis, 192pGoogle Scholar
  88. Tien Bui D, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444CrossRefGoogle Scholar
  89. Tien Bui D, 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 2012:1–26CrossRefGoogle Scholar
  90. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. In: Seppelt R, Voinov AA, Lange S, Bankamp D (eds) Proceedings of the iEMSs Sixth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2012) International Environmental Modelling and Software Society, Leipzig, Germany, July 2012Google Scholar
  91. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29CrossRefGoogle Scholar
  92. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012d) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211CrossRefGoogle Scholar
  93. Tien Bui D, Pradhan B, Lofman O, Revhaug I and Dick OB (2012e) 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: 28-40Google Scholar
  94. Tien Bui D, Ho TC, Revhaug I, Pradhan B, Nguyen D (2013a) Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based j48 decision tree classifier and its ensembles. In: Buchroithner M, Prechtel N, Burghardt D (eds) Cartography from pole to pole. Springer, Berlin, pp 303–317Google Scholar
  95. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick O (2013b) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Nat Hazards 66:707–730CrossRefGoogle Scholar
  96. Tien Bui D, Pradhan B, Revhaug I, Trung Tran C (2014) A comparative assessment between the application of fuzzy unordered rules induction algorithm and j48 decision tree models in spatial prediction of shallow landslides at Lang Son city, Vietnam. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote sensing applications in environmental research. Springer International Publishing, pp 87–111Google Scholar
  97. Tuan TA, Dan NT (2012) Landslide susceptibility mapping and zoning in the Son La hydropower catchment area using the analytical hierarchy process. J Sci Earth (Vietnamese): 223–232Google Scholar
  98. 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 54:9–22CrossRefGoogle Scholar
  99. 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 76:392–410CrossRefGoogle Scholar
  100. Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9:507–521CrossRefGoogle Scholar
  101. Van Westen CJ, Terlien MTJ (1996) An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth Surf Process Landf 21:853–868CrossRefGoogle Scholar
  102. Van TT, Anh DT, Hieu HH, Giap NX, Ke TD, Nam TD, Ngoc D, Ngoc DTY, Thai TN, Thang DV, Tinh NV, Tuat LT, Tung NT, Tuy PK, Viet HA (2006) Investigation and assessment of the current status and potential of landslides in some sections of the Ho Chi Minh Road, National Road 1a and proposed remedial measures to prevent landslides from threat of safety of people, property, and infrastructure. Vietnam Institute of Geosciences and Mineral Resources, Hanoi, p 249Google Scholar
  103. Vapnik VN (1998) Statistical learning theory. Wiley-InterscienceGoogle Scholar
  104. Vorpahl P, Elsenbeer H, Marker M, Schroder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39CrossRefGoogle Scholar
  105. Walter SD (2002) Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med 21:1237–1256CrossRefGoogle Scholar
  106. Witten IH, Frank E, Mark AH (2011) Data mining: practical machine learning tools and techniques (third edition). Morgan Kaufmann, BurlingtonGoogle Scholar
  107. Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31:2097–2110CrossRefGoogle Scholar
  108. Xiaomeng W, Borgelt C (2004) Information measures in fuzzy decision trees. Fuzzy Systems, 2004 Proceedings 2004 I.E. International Conference on, pp 85–90 vol. 81Google Scholar
  109. Xu L, Li J, Brenning A (2014) A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery. Remote Sens Environ 141:14–23CrossRefGoogle Scholar
  110. 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 101:572–582CrossRefGoogle Scholar
  111. Yem NT (2006) Assessment of landslides, flash floods, and debris flows in selected prone areas in the northern mountainous Vietnam and recommendation of remedial measures to prevent and mitigate potential damages. pp 166Google Scholar
  112. 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 35:1125–1138CrossRefGoogle Scholar
  113. 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 61:821–836CrossRefGoogle Scholar
  114. Zhuang L, Dai HH (2006) Parameter optimization of kernel-based one-class classifier on imbalance text learning. PRICAI 2006. Trends Artif Intell Proc 4099:434–443Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Dieu Tien Bui
    • 1
    • 2
    Email author
  • Tran Anh Tuan
    • 3
  • Harald Klempe
    • 1
  • Biswajeet Pradhan
    • 4
  • Inge Revhaug
    • 5
  1. 1.Geographic Information System Group, Department of Business Administration and Computer ScienceTelemark University CollegeBø i TelemarkNorway
  2. 2.Faculty of Surveying and MappingHanoi University of Mining and GeologyBac Tu LiemVietnam
  3. 3.Institute of Marine Geology and GeophysicsVietnam Academy of Science and TechnologyCau GiayVietnam
  4. 4.Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of EngineeringUniversity Putra MalaysiaSerdangMalaysia
  5. 5.Department of Mathematical Sciences and TechnologyNorwegian University of Life SciencesAasNorway

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