Evaluation and prediction of slope stability using machine learning approaches
Shan LIN1, Hong ZHENG1, Chao HAN1, Bei HAN1(), Wei LI2()
1. Key Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, China 2. School of Civil Engineering and Architecture, Linyi University, Linyi 276000, China
In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.
X He, S J Li, Y X Liu, Y P Zhou. Analyzing method of rock slope stability based on artificial neural network. Rock and Soil Mechanics, 2003, 24 : 73– 76
2
O Nawari, R Hartmann, R Lackner. Stability analysis of rock slopes with the direct sliding blocks method. International Journal of Rock Mechanics and Mining Sciences, 1997, 34( 3–4): 220–
3
B Thiebes, R Bell, T Glade, S Jäger, J Mayer, M Anderson, L Holcombe. Integration of a limit-equilibrium model into a landslide early warning system. Landslides, 2014, 11( 5): 859– 875 https://doi.org/10.1007/s10346-013-0416-2
4
A Johari, S Mousavi. An analytical probabilistic analysis of slopes based on limit equilibrium methods. Bulletin of Engineering Geology and the Environment, 2019, 78( 6): 4333– 4347 https://doi.org/10.1007/s10064-018-1408-1
5
M T Luan, Y Li, Q Yang. Discontinuous deformation computational mechanics model and its application to stability analysis of rock slope. Chinese Journal of Rock Mechanics and Engineering, 2000, 19( 3): 289– 294
6
Gitirana G Jr, Santos M A, Fredlund M D. Three-dimensional slope stability model using finite element stress analysis. In: Proceedings of GeoCongress 2008. New Orleans: ASCE, 2008, 191–198
7
G Sun, S Lin, H Zheng, Y Tan, T Sui. The virtual element method strength reduction technique for the stability analysis of stony soil slopes. Computers and Geotechnics, 2020, 119 : 103349– https://doi.org/10.1016/j.compgeo.2019.103349
8
R Trivedi, V Vishal, S P Pradhan, T N Singh, J C Jhanwar. Slope stability analysis in limestone mines. International Journal of Earth Sciences and Engineering, 2012, 5( 4): 759– 766
9
H Guo, H Zheng, X Zhuang. Numerical manifold method for vibration analysis of Kirchhoff’s plates of arbitrary geometry. Applied Mathematical Modelling, 2019, 66 : 695– 727 https://doi.org/10.1016/j.apm.2018.10.006
10
F Zheng, Y F Leung, J B Zhu, Y Y Jiao. Modified predictor-corrector solution approach for efficient discontinuous deformation analysis of jointed rock masses. International Journal for Numerical and Analytical Methods in Geomechanics, 2019, 43( 2): 599– 624 https://doi.org/10.1002/nag.2881
11
F Zheng, X Zhuang, H Zheng, Y Y Jiao, T Rabczuk. Kinetic analysis of polyhedral block system using an improved potential-based penalty function approach for explicit discontinuous deformation analysis. Applied Mathematical Modelling, 2020, 82 : 314– 335 https://doi.org/10.1016/j.apm.2020.01.026
12
X Zhuang, F Zheng, H Zheng, Y Y Jiao, T Rabczuk, P Wriggers. A cover-based contact detection approach for irregular convex polygons in discontinuous deformation analysis. International Journal for Numerical and Analytical Methods in Geomechanics, 2021, 45( 2): 208– 233 https://doi.org/10.1002/nag.3157
13
S Zhou, T Rabczuk, X Zhuang. Phase-field modeling of quasi-static and dynamic crack propagation: COMSOL implementation and case studies. Advances in Engineering Software, 2018, 122 : 31– 49 https://doi.org/10.1016/j.advengsoft.2018.03.012
14
S Zhou, X Zhuang, T Rabczuk. A phase-field modeling approach of fracture propagation in poroelastic media. Engineering Geology, 2018, 240 : 189– 203 https://doi.org/10.1016/j.enggeo.2018.04.008
15
S Zhou, X Zhuang, H Zhu, T Rabczuk. Phase field modeling of crack propagation, branching and coalescence in rocks. Theoretical and Applied Fracture Mechanics, 2018, 96 : 174– 192 https://doi.org/10.1016/j.tafmec.2018.04.011
16
Fielding A H. Machine Learning Methods for Ecological Applications. Boston: Springer, 1999, 1–35
17
H Moayedi, S Hayati. Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Applied Soft Computing, 2018, 66 : 208– 219 https://doi.org/10.1016/j.asoc.2018.02.027
18
C Anitescu, E Atroshchenko, N Alajlan, T Rabczuk. Artificial neural network methods for the solution of second-order boundary value problems. Computers, Materials and Continua, 2019, 59( 1): 345– 359 https://doi.org/10.32604/cmc.2019.06641
19
N D Hoang, A D Pham. Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis. Expert Systems with Applications, 2016, 46 : 60– 68 https://doi.org/10.1016/j.eswa.2015.10.020
20
Z Liu, J Shao, W Xu, H Chen, Y Zhang. An extreme learning machine approach for slope stability evaluation and prediction. Natural Hazards, 2014, 73( 2): 787– 804 https://doi.org/10.1007/s11069-014-1106-7
U C Kothari, M Momayez. Machine learning: A novel approach to predicting slope instabilities. International Journal of Geophysics, 2018, 1– 9 https://doi.org/10.1155/2018/4861254
23
Eberhart R. Neural Network PC Tools: A Practical Guide. San Diego: Academic Press, 1990
24
S K Das, R K Biswal, N Sivakugan, B Das. Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences, 2011, 64( 1): 201– 210 https://doi.org/10.1007/s12665-010-0839-1
25
Y Erzin, T Cetin. The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Computers & Geosciences, 2013, 51 : 305– 313 https://doi.org/10.1016/j.cageo.2012.09.003
26
K Gelisli, T Kaya, A E Babacan. Assessing the factor of safety using an artificial neural network: Case studies on landslides in Giresun, Turkey. Environmental Earth Sciences, 2015, 73( 12): 1– 8 https://doi.org/10.1007/s12665-015-4027-1
27
C I Wu, H Y Kung, C H Chen, L C Kuo. An intelligent slope disaster prediction and monitoring system based on WSN and ANP. Expert Systems with Applications, 2014, 41( 10): 4554– 4562 https://doi.org/10.1016/j.eswa.2013.12.049
28
N D Hoang, D T Bui. Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine. Handbook of Neural Computation, 2017, 333– 344
29
M Koopialipoor, D J Armaghani, A Hedayat, A Marto, B Gordan. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing, 2019, 23( 14): 5913– 5929 https://doi.org/10.1007/s00500-018-3253-3
30
H B Wang, K Sassa. Rainfall-induced landslide hazard assessment using artificial neural networks. Earth Surface Processes and Landforms, 2006, 31( 2): 235– 247 https://doi.org/10.1002/esp.1236
31
B Pradhan, S Lee, M F Buchroithner. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, 2010, 34( 3): 216– 235 https://doi.org/10.1016/j.compenvurbsys.2009.12.004
32
C Melchiorre, M Matteucci, A Azzoni, A Zanchi. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 2008, 94( 3–4): 379– 400 https://doi.org/10.1016/j.geomorph.2006.10.035
33
A J Li, S Khoo, A V Lyamin, Y Wang. Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Automation in Construction, 2016, 65( 5): 42– 50 https://doi.org/10.1016/j.autcon.2016.02.004
34
B Pradhan. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 2013, 51 : 350– 365 https://doi.org/10.1016/j.cageo.2012.08.023
35
X Z Shi, J Zhou, W Zheng, H Y Hu, H Y Wang. Bayes discriminant analysis method and its application for prediction of slope stability. Journal of Sichuan University: Engineering Science Edition, 2010, 42( 3): 63– 68
36
Yan X M, Li X B. Bayes discriminant analysis method for predicting the stability of open pit slope. In: International Conference on Electric Technology & Civil Engineering. Lushan: IEEE, 2011
37
M Y Cheng, N D Hoang. Slope collapse prediction using Bayesian framework with k-nearest neighbor density estimation: Case-study in Taiwan. Journal of Computing in Civil Engineering, 2016, 30( 1): 04014116– https://doi.org/10.1061/(ASCE)CP.1943-5487.0000456
38
X Z Li, J M Kong, C H Wang. Application of multi-classification support vector machine in the identifying of landslide stability. Journal of Jilin University, 2010, 40( 3): 631– 637
39
H Zhao, S Yin, Z Ru. Relevance vector machine applied to slope stability analysis. International Journal for Numerical and Analytical Methods in Geomechanics, 2012, 36( 5): 643– 652 https://doi.org/10.1002/nag.1037
40
J Zhou, E Li, S Yang, M Wang, X Shi, S Yao, H S Mitri. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science, 2019, 118 : 505– 518 https://doi.org/10.1016/j.ssci.2019.05.046
41
C Qi, X Tang. Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study. Computers & Industrial Engineering, 2018, 118 : 112– 122 https://doi.org/10.1016/j.cie.2018.02.028
Seo Y K. Computational methods for elastoplastic slope stability analysis with seepage. Dissertation for the Doctoral Degree. Iowa City: University of Iowa, 1998
45
N K Sah, P R Sheorey, L N Upadhyaya. Maximum likelihood estimation of slope stability. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1994, 31( 1): 47– 53 https://doi.org/10.1016/0148-9062(94)92314-0
46
H Fattahi. Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods. Journal of Mining and Environment, 2017, 8( 2): 163– 177
47
H B Wang, W Y Xu, R C Xu. Slope stability evaluation using backpropagation neural networks. Engineering Geology, 2005, 80( 3–4): 302– 315 https://doi.org/10.1016/j.enggeo.2005.06.005
48
A Manouchehrian, J Gholamnejad, M Sharifzadeh. Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environmental Earth Sciences, 2014, 71( 3): 1267– 1277 https://doi.org/10.1007/s12665-013-2531-8
Li J, Wang F. Study on the forecasting models of slope stability under data mining. In: The 12th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments. Honolulu: ASCE, 2010, 765–776
51
K Gelisli, T Kaya, A E Babacan. Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey. Environmental Earth Sciences, 2015, 73( 12): 1– 8 https://doi.org/10.1007/s12665-015-4027-1
52
A Chakraborty, D Goswami. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences, 2017, 10( 17): 1– 11 https://doi.org/10.1007/s12517-017-3167-x
53
X T Feng, Y J Wang, S Z Lu. Neural network estimation of slope stability. Journal of Engineering Geology, 1995, 3( 4): 54– 61
54
Kostić S, Vasović N, Todorović K, Samčović A. Application of artificial neural networks for slope stability analysis in geotechnical practice. In: 2016 13th Symposium on Neural Networks and Applications (NEUREL). Belgrade: IEEE, 2016, 1–6
55
H Fattahi. Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods. Journal of Mining and Environment, 2017, 8( 2): 163– 177
56
Z Zhang, Z Liu, L Zheng, Y Zhang. Development of an adaptive relevance vector machine approach for slope stability inference. Neural Computing & Applications, 2014, 25( 7−8): 2025– 2035 https://doi.org/10.1007/s00521-014-1690-1
57
S S Band, S Janizadeh, S Saha, K Mukherjee, S K Bozchaloei, A Cerdà, M Shokri, A Mosavi. Evaluating the efficiency of different regression, decision tree, and bayesian machine learning algorithms in spatial piping erosion susceptibility using ALOS/PALSAR data. Land (Basel), 2020, 9( 10): 346– 368 https://doi.org/10.3390/land9100346
58
Tang Z H, Maclennan J. Data Mining with SQL Server 2005. Indianapolis: Wiley Publishing, Inc., 2005
59
A Akgun. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, Turkey. Landslides, 2012, 9( 1): 93– 106 https://doi.org/10.1007/s10346-011-0283-7
60
J O Ogutu, T Schulz-Streeck, H P Piepho. Genomic selection using regularized linear regression models: Ridge regression, lasso, elastic net and their extensions. BMC Proceedings, 2012, 6( 2): 1– 6
61
Kramer O. K-Nearest Neighbors. Berlin: Springer, 2013
62
Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. NewYork: Association for Computing Machinery, 1992, 144–152
63
A J Myles, R N Feudale, Y Liu, N A Woody, S D Brown. An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 2004, 18( 6): 275– 285
64
S K Murthy. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery, 1998, 2( 4): 345– 389 https://doi.org/10.1023/A:1009744630224
J H Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29( 5): 1189– 1232 https://doi.org/10.1214/aos/1013203451
67
X Y Li, W D Li, X Yan. Human age prediction based on DNA methylation using a gradient boosting regressor. Genes, 2018, 9( 9): 424– 439 https://doi.org/10.3390/genes9090424
68
Simm J, Abril I M. ExtraTrees: Extremely Randomized Trees (Extra Trees) Method for Classification and Regression. R Package Version 1.0. 5. 2014
69
J R Landis, G G Koch. The measurement of observer agreement for categorical data. Biometrics, 1977, 33( 1): 159– 174 https://doi.org/10.2307/2529310
