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Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction

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Abstract

Slope engineering is a complex nonlinear system. It is difficult to respond with a high level of precision and efficiency requirements for stability assessment using conventional theoretical analysis and numerical computation. An ensemble learning algorithm for solving highly nonlinear problems is introduced in this paper to study the stability of 444 slope cases. Different ensemble learning methods [AdaBoost, gradient boosting machine (GBM), bagging, extra trees (ET), random forest (RF), hist gradient boosting, voting and stacking] for slope stability assessment are studied and compared to make the best use of the large variety of existing statistical and ensemble learning methods collected. Six potential relevant indicators, \(\gamma\), C, \(\varphi\), \(\beta\), H and \(r_{u}\), are chosen as the prediction indicators. The tenfold CV method is used to improve the generalization ability of the classification models. By analysing the evaluation indicators AUC, accuracy, kappa value and log loss, the stacking model shows the best performance with the highest AUC (0.9452), accuracy (84.74%), kappa value (0.6910) and lowest log loss (0.3282), followed by ET, RF, GBM and bagging models. The analysis of engineering examples shows that the ensemble learning algorithm can deal with this relationship well and give accurate and reliable prediction results, which has good applicability for slope stability evaluation. Additionally, geotechnical material variables are found to be the most influential variables for slope stability prediction.

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References

  1. Aljamaan H, Alazba A (2020) Software defect prediction using tree-based ensembles. In: Proceedings of the 16th ACM international conference on predictive models and data analytics in software engineering, pp 1–10

  2. Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T (2019) Artificial neural network methods for the solution of second-order boundary value problems. Comput Mater Contin 59(1):345–359

    Google Scholar 

  3. Bradley P (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159

    Article  Google Scholar 

  4. Breiman L (1996) Bagging predictors machine learning. Mach Learn 24:123–140

    Article  MATH  Google Scholar 

  5. Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10(17):385

    Article  Google Scholar 

  6. Chen L, Peng Z, Chen W, Peng W, Wu Q (2009) Artificial neural network simulation on prediction of clay slope stability based on fuzzy controller. J Cent South Univ (Sci Technol) 40(5):1381–1387

    Google Scholar 

  7. Chen C, Xiao Z, Zhang G (2011) Stability assessment model for epimetamorphic rock slopes based on adaptive neuro-fuzzy inference system. Electron J Geotech Eng 16:93–107

    Google Scholar 

  8. Cheng M, Hoang ND (2015) Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier. Nat Hazards 78(3):1961–1978

    Article  Google Scholar 

  9. Dickson M, Perry GLW (2016) Identifying the controls on coastal cliff landslides using machine-learning approaches. Environ Model Softw 76:117–127

    Article  Google Scholar 

  10. Dietterich TG (1997) Machine learning research: four current directions. AI Mag 18(4):97–136

    Google Scholar 

  11. Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139–157

    Article  Google Scholar 

  12. Drucker H, Schapire R, Simard P (1993) Boosting performance in neural networks. Int J Pattern Recogn Artif Intell 7(04):705–719

    Article  Google Scholar 

  13. Duncan JM (1996) State of the art: limit equilibrium and finite-element analysis of slopes. J Geotech Eng 123(7):577–596

    Article  Google Scholar 

  14. Fattahi H (2017) Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods. J Min Environ 8(2):163–177

    MathSciNet  Google Scholar 

  15. Feng X, Hudson J (2004) The ways ahead for rock engineering design methodologies. Int J Rock Mech Min Sci 41(2):255–273

    Article  Google Scholar 

  16. Feng X, Wang Y, Lu S (1995) Neural network estimation of slope stability. J Eng Geol 3(4):54–61

    Google Scholar 

  17. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  18. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  MathSciNet  MATH  Google Scholar 

  19. Gounaridis D, Koukoulas S (2016) Urban land cover thematic disaggregation, employing datasets from multiple sources and RandomForests modeling. Int J Appl Earth Observ Geoinf 51:1–10

    Google Scholar 

  20. Griffiths D, Lane P (1999) Slope stability analysis by finite elements. Geotechnique 49(3):387–403

    Article  Google Scholar 

  21. Gutta S, Wechsler H (1996) Face recognition using hybrid classifier systems. In: Proceedings of international conference on neural networks (ICNN'96). IEEE.

  22. He F, Wu S, Zhang Y, Bao H (2004) A neural network method for analyzing compass slope stability of the highway. Acta Geosici Sin 25(1):95–98

    Google Scholar 

  23. Hoang ND, Pham AD (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: a multinational data analysis. Expert Syst Appl 46:60–68

    Article  Google Scholar 

  24. Hong H, Liu J, Bui D, Pradhan B, Acharya TD, Pham B, Zhu A, Chen W, Ahmad B (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). CATENA 163:399–413

    Article  Google Scholar 

  25. Hosmer J, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. Wiley, New York

    Book  MATH  Google Scholar 

  26. Jin L, Feng W, Zhang J (2004) Maximum likelihood estimation on safety coefficiefficients of rocky slope near dam of Fengtan project. Chin J Rock Mech Eng 23(11):1891–1894

    Google Scholar 

  27. Kang F, Li J, Ma Z (2013) An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Eng Optim 45(2):207–223

    Article  MathSciNet  Google Scholar 

  28. Kuhn M, Johnson K (2013) Applied predictive modeling. Springer, Berlin

    Book  MATH  Google Scholar 

  29. Lee T, Lin H, Lu Y (2009) Assessment of highway slope failure using neural networks. J Zhejiang Univ 10(1):101–108

    Article  MATH  Google Scholar 

  30. Li J, Wang F (2010) Study on the forecasting models of slope stability under data mining. In: Earth and space 2010: engineering, science, construction, and operations in challenging environments, pp 765–776

  31. Li W, Yang S, Chen E, Qiao J, Dai L (2006) Neural network method of analysis of natural slope failure due to underground mining in mountainous areas. Yantu Lixue (Rock Soil Mech) 27(9):1563–1566

    Google Scholar 

  32. Lin H, Chang S, Wu J, Juang H (2009) Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: pre-and post-earthquake investigation. Eng Geol 104(3–4):280–289

    Article  Google Scholar 

  33. Lin S, Zheng H, Jiang W, Li W, Sun G (2020) Investigation of the excavation of stony soil slopes using the virtual element method. Eng Anal Bound Elem 121:76–90

    Article  MathSciNet  MATH  Google Scholar 

  34. Lin Y, Zhou K, Li J (2018) Prediction of slope stability using four supervised learning methods. IEEE Access 6:31169–31179

    Article  Google Scholar 

  35. Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73(2):787–804

    Article  Google Scholar 

  36. Lu P, Rosenbaum M (2003) Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 30(3):383–398

    Article  Google Scholar 

  37. Manouchehrian A, Gholamnejad J, Sharifzadeh M (2014) Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environ Earth Sci 71(3):1267–1277

    Article  Google Scholar 

  38. Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234

    Article  Google Scholar 

  39. Michalowski LR (1995) Slope stability analysis: a kinematical approach. Geotechnique 45(2):283–293

    Article  MathSciNet  Google Scholar 

  40. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  41. Qi C, Tang X (2018) A hybrid ensemble method for improved prediction of slope stability. Int J Numer Anal Methods Geomech 42(15):1823–1839

    Article  Google Scholar 

  42. Qi C, Tang X (2018) Slope stability prediction using integrated metaheuristic and machine learning approaches: a comparative study. Comput Ind Eng 118:112–122

    Article  Google Scholar 

  43. Sakellariou M, Ferentinou M (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23(4):419–445

    Article  Google Scholar 

  44. Samui P (2008) (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56(2):255–267

    Article  Google Scholar 

  45. Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336

    Article  MATH  Google Scholar 

  46. Shimshoni Y, Intrator N (1998) Classification of seismic signals by integrating ensembles of neural networks. IEEE Trans Signal Process 46(5):1194–1201

    Article  Google Scholar 

  47. Simm J, Abril I (2014) Extratrees: extremely randomized trees (ExtraTrees) method for classification and regression. R package version 1.0. 5.

  48. Sun G, Lin S, Zheng H, Tan Y, Sui T (2020) The virtual element method strength reduction technique for the stability analysis of stony soil slopes. Comput Geotech 119:103349

    Article  Google Scholar 

  49. Wang CH (2004) Study on prediction methods for high engineering slope. Master thesis

  50. Wang L, Wu C, Tang L, Zhang W, Lacasse S, Liu H, Gao L (2020) Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method. Acta Geotech 15(11):3135–3150

    Article  Google Scholar 

  51. Wang H, Xu W, Xu R (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80(3–4):302–315

    Article  Google Scholar 

  52. Wen S, La H, Wang C (2013) Analysis of influence factors of slope stability. Appl Mech Mater Trans Tech Publ 256:34–38

    Google Scholar 

  53. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259

    Article  Google Scholar 

  54. Xiao Z, Chen C, Ji Y (2011) Applying adaptive neuro-fuzzy inference system to stability assessment of reservoir slope. Bull Soil Water Conserv 31(5):186–190

    Google Scholar 

  55. Xu W, Shao J (1998) Artificial neural network analysis for the evaluation of slope stability. Application of numerical methods to geotechnical problems. Springer, Berlin, pp 665–672

    MATH  Google Scholar 

  56. Xu F, Xu W, Wang K (2009) Slope stability analysis using least square support vector machine optimized with ant colony algorithm. J Eng Geol 17(2):253–257

    Google Scholar 

  57. Yan X, Li X (2011) Bayes discriminant analysis method for predicting the stability of open pit slope. In: 2011 International conference on electric technology and civil engineering (ICETCE). IEEE, pp 147–150

  58. Yun L, Keping Z, Jielin L (2018) Prediction of slope stability using four supervised learning methods. IEEE Access 6:31169–31179

    Article  Google Scholar 

  59. Zhao H, Yin S, Ru Z (2012) Relevance vector machine applied to slope stability analysis. Int J Numer Anal Meth Geomech 36(5):643–652

    Article  Google Scholar 

  60. Zheng F, Leung YF, Zhu J, Jiao Y (2019) Modified predictor-corrector solution approach for efficient discontinuous deformation analysis of jointed rock masses. Int J Numer Anal Meth Geomech 43(2):599–624

    Article  Google Scholar 

  61. Zheng F, Zhuang X, Zheng H, Jiao Y, Timon R (2020) Kinetic analysis of polyhedral block system using an improved potential-based penalty function approach for explicit discontinuous deformation analysis. Appl Math Model 82:314–335

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhou Z, Jiang Y, Yang Y, Chen S (2002) Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 24(1):25–36

    Article  Google Scholar 

  63. Zhou J, Li X, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79(1):291–316

    Article  Google Scholar 

  64. Zhou J, Li E, Yang S, Wang M, Mitri H (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Article  Google Scholar 

  65. Zhou S, Rabczuk T, Zhuang X (2018) Phase field modeling of quasi-static and dynamic crack propagation: COMSOL implementation and case studies. Adv Eng Softw 122:31–49

    Article  Google Scholar 

  66. Zhou S, Zhuang X, Rabczuk T (2018) A phase-field modeling approach of fracture propagation in poroelastic media. Eng Geol 240:189–203

    Article  Google Scholar 

  67. Zhou S, Zhuang X, Zhu H, Rabczuk T (2018) Phase field modeling of crack propagation, branching and coalescence in rocks. Theor Appl Fract Mech 96:174–192

    Article  Google Scholar 

  68. Zhu C (2005) Analysis and evaluation of slope stability—taking yuanmo expressway slope as an example. Kunming University of Science and Technology

  69. Zhu B, Zhou D, Chen S, Wang L (2011) Evaluation of slope stability by improved BP neural network with L-M method. West-China Explor Eng 10:21–24

    Google Scholar 

  70. Zhuang X, Zheng F, Zheng H, Jiao Y, Rabczuk T, Wriggers P (2021) A cover-based contact detection approach for irregular convex polygons in discontinuous deformation analysis. Int J Numer Anal Meth Geomech 45:208–233

    Article  Google Scholar 

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Acknowledgements

Supported by Open Research Fund of the State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, and Chinese Academy of Sciences (Z019008), the Natural Science Foundation of China (Grant Nos. 42107214, 11972043 and 11902134)

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Authors and Affiliations

  1. Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing, 100124, China

    Shan Lin, Hong Zheng, Bei Han, Yanyan Li & Chao Han

  2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China

    Shan Lin

  3. School of Civil Engineering and Architecture, Linyi University, Linyi, 276000, China

    Wei Li

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  1. Shan Lin
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Correspondence to Bei Han.

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Table 3 Slope stability prediction database
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Lin, S., Zheng, H., Han, B. et al. Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotech. 17, 1477–1502 (2022). https://doi.org/10.1007/s11440-021-01440-1

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