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Prediction of Slope Stability using Naive Bayes Classifier

  • Probabilistic Learning for Decision-making on Civil Infrastructures
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Slope stability prediction is of primary concern in identifying terrain that is susceptible to landslides and mitigating the damages caused by landslides. In this study, a Naive Bayes Classifier (NBC) was employed to predict slope stability for a slope subjected to circular failures, based on six input factors: slope height (H), slope angle (α), cohesion (c), friction angle (φ), unit weight (γ), and pore pressure ratio (r u ). An expectation maximization algorithm was used to perform parameter learning for the NBC with an incomplete data set of 69 slope cases. The model validation with 13 new cases shows that, when compared to the existing empirical approach, the proposed NBC model yields better performance in terms of both accuracy and applicability (i.e., the NBC allows us to determine the probability of slope stability based on any subset of the six input factors).

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References

  • Alimohammadlou, Y., Najafi, A., and Gokceoglu, C. (2014). “Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province, Iran.” CATENA, Vol. 120, pp. 149–162, DOI: https://doi.org/10.1016/j.catena.2014.04.009.

    Article  Google Scholar 

  • Bhargavi, P. and Jyothi, S. (2009). “Applying naive bayes data mining technique for classification of agricultural land soils.” International Journal of Computer Science and Network Security, Vol. 9, No. 8, pp. 117–122.

    Google Scholar 

  • Bui, D. T., Pradhan, B., Lofman, O., and Revhaug, I. (2012). “Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and naïve bayes models.” Mathematical Problems in Engineering, Vol. 2012, pp. 1–26, DOI: https://doi.org/10.1155/2012/974638.

    Google Scholar 

  • Bye, A. and Bell, F. (2001). “Stability assessment and slope design at Sandsloot open pit, South Africa.” International Journal of Rock Mechanics and Mining Sciences, Vol. 38, No. 3, pp. 449–466.

    Article  Google Scholar 

  • Cai, F. and Ugai, K. (2004). “Numerical analysis of rainfall effects on slope stability.” International Journal of Geomechanics, Vol. 4, No. 2, pp. 69–78, DOI: https://doi.org/10.1061/(ASCE)1532-3641(2004)4:2(69).

    Article  Google Scholar 

  • Chen, W.-F. (1975). Limit Analysis and Soil Plasticity, Developments in Geotechnical Engineering, Elsevier Scientific Publishing Co., New York, pp. 47–156.

    Google Scholar 

  • Chen, W., Xie, X., Peng, J., Wang, J., Duan, Z., and Hong, H. (2017). “GIS-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models.” Geomatics, Natural Hazards and Risk, pp. 1–24, DOI: https://doi.org/10.1080/19475705.2017.1289250.

    Google Scholar 

  • Choobbasti, A., Farrokhzad, F., and Barari, A. (2009). “Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran).” Arabian Journal of Geosciences, Vol. 2, No. 4, pp. 311–319, DOI: https://doi.org/10.1007/S12517-009-0035-3.

    Article  Google Scholar 

  • Das, S. K., Biswal, R. K., Sivakugan, N., and Das, B. (2011). “Classification of slopes and prediction of factor of safety using differential evolution neural networks.” Environmental Earth Sciences, Vol. 64, No. 1, pp. 201–210, DOI: https://doi.org/10.1007/S12665-010-0839-1.

    Article  Google Scholar 

  • Dawson, E. M., Roth, W. H., and Drescher, A. (1999). “Slope stability analysis by strength reduction.” Géotechnique, Vol. 49, No. 6, pp. 835–840, DOI: https://doi.org/10.1680/geot.1999.49.6.835.

    Article  Google Scholar 

  • Domingos, P. and Pazzani, M. (1997). “On the optimality of the simple bayesian classifier under Zero-One loss.” Machine Learning, Vol. 29, Nos. 2–3, pp. 103–130, DOI: https://doi.org/10.1023/a:1007413511361.

    Article  MATH  Google Scholar 

  • Erzin, Y. and Cetin, T. (2013). “The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions.” Computers & Geosciences, Vol. 51, pp. 305–313, DOI: https://doi.org/10.1016/j.cageo.2012.09.003.

    Article  Google Scholar 

  • Feng, X. (2000). Introduction of Intelligent Rock Mechanics, Science Press, Beijing, China, pp. 239–241.

    Google Scholar 

  • Feng, X. and Jimenez, R. (2015). “Predicting tunnel squeezing with incomplete data using Bayesian networks.” Engineering Geology, Vol. 195, pp. 214–224, DOI: https://doi.org/10.1016/j.enggeo.2015.06.017.

    Article  Google Scholar 

  • Ferentinou, M. D. and Sakellariou, M. G. (2007). “Computational intelligence tools for the prediction of slope performance.” Computers and Geotechnics, Vol. 34, No. 5, pp. 362–384, DOI: https://doi.org/10.1016/j.compgeo.2007.06.004.

    Article  Google Scholar 

  • Friedman, N., Geiger, D., and Goldszmidt, M. (1997). “Bayesian network classifiers.” Machine Learning, Vol. 29, Nos. 2–3, pp. 131–163, DOI: https://doi.org/10.1023/A:1007465528199.

    Article  MATH  Google Scholar 

  • Gordan, B., Jahed Armaghani, D., Hajihassani, M., and Monjezi, M. (2016). “Prediction of seismic slope stability through combination of particle swarm optimization and neural network.” Engineering with Computers, Vol. 32, No. 1, pp. 85–97, DOI: https://doi.org/10.1007/s00366-015-0400-7.

    Article  Google Scholar 

  • Griffiths, D. and Lane, P. (1999). “Slope stability analysis by finite elements.” Géotechnique, Vol. 49, No. 3, pp. 387–403, DOI: https://doi.org/10.1680/geot.1999.49.3.387.

    Article  Google Scholar 

  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). “The WEKA data mining software: An update.” SIGKDD Explorations, Vol. 11, No. 1, pp. 10–18, DOI: https://doi.org/10.1145/1656274.1656278.

    Article  Google Scholar 

  • Hand, D. J. and Yu, K. (2001). “Idiot’s Bayes—not so stupid after all?.” International Statistical Review, Vol. 69, No. 3, pp. 385–398, DOI: https://doi.org/10.2307/1403452.

    MATH  Google Scholar 

  • Hoang, N.-D. and Pham, A.-D. (2016). “Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis.” Expert Systems with Applications, Vol. 46, pp. 60–68, DOI: https://doi.org/10.1016/j.eswa.2015.10.020.

    Article  Google Scholar 

  • Hoek, E. and Bray, J. W. (1981). Rock Slope Engineering (3rd edition), Institution of Mining and Metallurgy, London, pp. 226–247.

    Google Scholar 

  • Huang, Z., Cui, J., and Liu, H. (2004). “Chaotic neural network method for slope stability prediction.” Chinese Journal of Rock Mechanics and Engineering, Vol. 22, pp. 015.

    Google Scholar 

  • Jensen, F. V. and Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs (2nd ed.), Springer, New York, pp. 201–207.

    Book  MATH  Google Scholar 

  • Kalatehjari, R., Ali, N., Kholghifard, M., and Hajihassani, M. (2014). “The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization.” Arabian Journal of Geosciences, Vol. 7, No. 4, pp. 1529–1539, DOI: https://doi.org/10.1007/s12517-013-0922-5.

    Article  Google Scholar 

  • Kontkanen, P., Myllymäki, P., Silander, T., and Tirri, H. (1997). “Comparing predictive inference methods for discrete domains.” Proceedings of the sixth International Workshop on Artificial Intelligence and Statistics, Ft. Lauderdale, USA, pp. 311–318.

    Google Scholar 

  • Korb, K. B. and Nicholson, A. E. (2004). Bayesian Artificial Intelligence, CRC Press, London, pp. 180–185.

    MATH  Google Scholar 

  • Lenchman, J. B. and Griffiths, D. V. (2000). “Analysis of the progression of failure of the earth slopes by finite elements.” Slope Stability 2000: Proceedings of Sessions of Geo-Denver 2000, ASCE, Denver, pp. 250–265.

    Chapter  Google Scholar 

  • Li, N., Feng, X., and Jimenez, R. (2017). “Predicting rock burst hazard with incomplete data using Bayesian networks.” Tunnelling and Underground Space Technology, Vol. 61, pp. 61–70, DOI: https://doi.org/10.1016/j.tust.2016.09.010.

    Article  Google Scholar 

  • Li, X. (2004). Comparative studies of artificial neural networks and adaptive Neuro-Fuzzy inference system based approach for the circular sliding slopes stability analysis, Master Thesis, University of South China, Hengyang, Hunan, China.

    Google Scholar 

  • Li, X. and Kong, J. (2014). “Application of GA-SVM method with parameter optimization for landslide development prediction.” Natural Hazards and Earth System Sciences, Vol. 14, No. 3, pp. 525–533, DOI: https://doi.org/10.5194/nhess-14-525-2014.

    Article  Google Scholar 

  • Lin, P. S., Lin, M. H., Su, M. B., and Lee, T. M. (1988). “An investigation on the failure of a building constructed on hillslope.” Proceedings of 2nd International Symposium on Field Measurements in Geomechanics, A A Balkema, Kobe, pp. 445–449.

    Google Scholar 

  • Liu, Z., Shao, J., Xu, W., Chen, H., and Zhang, Y. (2014). “An extreme learning machine approach for slope stability evaluation and prediction.” Natural Hazards, Vol. 73, No. 2, pp. 787–804, DOI: https://doi.org/10.1007/s11069-014-1106-7.

    Article  Google Scholar 

  • Lu, P. and Rosenbaum, M. S. (2003). “Artificial neural networks and grey systems for the prediction of slope stability.” Natural Hazards, Vol. 30, No. 3, pp. 383–398, DOI: https://doi.org/10.1023/B:NHAZ.0000007168.00673.27.

    Article  Google Scholar 

  • Madzic, E. (1988). “Stability of unstable final slope in deep open iron mine.” Proceedings of 2nd International Symposium on Field Measurements in Geomechanics, A A Balkema, Kobe, pp. 455–458.

    Google Scholar 

  • Michalowski, R. (1995). “Slope stability analysis: A kinematical approach.” Géotechnique, Vol. 45, No. 2, pp. 283–293, DOI: https://doi.org/10.1680/geot.1995.45.2.283.

    Article  MathSciNet  Google Scholar 

  • Norsys Software Corporation (1998). Netica Application User’s Guide, Norsys Software Corporation, Vancouver, BC, Canada, pp. 70–75.

    Google Scholar 

  • Pham, B. T., Tien Bui, D., Pourghasemi, H. R., Indra, P., and Dholakia, M. B. (2015). “Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods.” Theoretical and Applied Climatology, Vol. 128, Nos. 1–2, pp. 255–273, DOI: https://doi.org/10.1007/s00704-015-1702-9.

    Google Scholar 

  • Rukhaiyar, S., Alam, M., and Samadhiya, N. (2017). “A PSO-ANN hybrid model for predicting factor of safety of slope.” International Journal of Geotechnical Engineering, pp. 1–11, DOI: https://doi.org/10.1080/19386362.2017.1305652.

    Google Scholar 

  • Sah, N., Sheorey, P., and Upadhyaya, L. (1994). “Maximum likelihood estimation of slope stability.” International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, Vol. 31, No. 1, pp. 47–53, DOI: https://doi.org/10.1016/0148-9062(94)92314-0.

    Article  Google Scholar 

  • Sakellariou, M. and Ferentinou, M. (2005). “A study of slope stability prediction using neural networks.” Geotechnical & Geological Engineering, Vol. 23, No. 4, pp. 419–445, DOI: https://doi.org/10.1007/s10706-004-8680-5.

    Article  Google Scholar 

  • Samui, P. (2008). “Slope stability analysis: A support vector machine approach.” Environmental Geology, Vol. 56, No. 2, pp. 255–267, DOI: https://doi.org/10.1007/s00254-007-1161-4.

    Article  Google Scholar 

  • Shirzadi, A., Bui, D. T., Pham, B. T., Solaimani, K., Chapi, K., Kavian, A., Shahabi, H., and Revhaug, I. (2017). “Shallow landslide susceptibility assessment using a novel hybrid intelligence approach.” Environmental Earth Sciences, Vol. 76, No. 2, pp. 1–18, DOI: https://doi.org/10.1007/s12665-016-6374-y.

    Article  Google Scholar 

  • Song, Y., Gong, J., Gao, S., Wang, D., Cui, T., Li, Y., and Wei, B. (2012). “Susceptibility assessment of earthquake-induced landslides using Bayesian network: A case study in Beichuan, China.” Computers & Geosciences, Vol. 42, pp. 189–199, DOI: https://doi.org/10.1016/j.cageo.2011.09.011.

    Article  Google Scholar 

  • Stead, D., Eberhardt, E., and Coggan, J. S. (2006). “Developments in the characterization of complex rock slope deformation and failure using numerical modelling techniques.” Engineering Geology, Vol. 83, No. 1, pp. 217–235, DOI: https://doi.org/10.1016/j.enggeo.2005.06.033.

    Article  Google Scholar 

  • Suman, S., Khan, S., Das, S., and Chand, S. (2016). “Slope stability analysis using artificial intelligence techniques.” Natural Hazards, Vol. 84, No. 2, pp. 727–748, DOI: https://doi.org/10.1007/s11069-016-2454-2.

    Article  Google Scholar 

  • Taheri, A. and Tani, K. (2010). “Assessment of the stability of rock slopes by the slope stability rating classification system.” Rock Mechanics and Rock Engineering, Vol. 43, No. 3, pp. 321–333, DOI: https://doi.org/10.1007/s00603-009-0050-4.

    Article  Google Scholar 

  • Thiebes, B., Bell, R., Glade, T., Jäger, S., Mayer, J., Anderson, M., and Holcombe, L. (2014). “Integration of a limit-equilibrium model into a landslide early warning system.” Landslides, Vol. 11, No. 5, pp. 859–875, DOI: https://doi.org/10.1007/s10346-013-0416-2.

    Article  Google Scholar 

  • Ting, S. L., Ip, W. H., and Tsang, A. H. C. (2011). “Is naïve bayes a good classifier for document classification?.” International Journal of Software Engineering and Its Applications, Vol. 5, No. 3, pp. 37–46.

    Google Scholar 

  • Tran, C. and Srokosz, P. (2010). “The idea of PGA stream computations for soil slope stability evaluation.” Comptes Rendus Mécanique, Vol. 338, No. 9, pp. 499–509, DOI: https://doi.org/10.1016/j.crme.2010.08.001.

    Article  MATH  Google Scholar 

  • Tsangaratos, P. and Ilia, I. (2016). “Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size.” CATENA, Vol. 145, pp. 164–179, DOI: https://doi.org/10.1016/j.catena.2016.06.004.

    Article  Google Scholar 

  • Uusitalo, L. (2007). “Advantages and challenges of Bayesian networks in environmental modelling.” Ecological Modelling, Vol. 203, Nos. 3–4, pp. 312–318, DOI: https://doi.org/10.1016/j.ecolmodel.2006.11.033.

    Article  Google Scholar 

  • Verma, A., Singh, T., Chauhan, N. K., and Sarkar, K. (2016). “A hybrid FEM-ANN approach for slope instability prediction.” Journal of The Institution of Engineers (India): Series A, Vol. 97, No. 3, pp. 171–180, DOI: https://doi.org/10.1007/s40030-016-0168-9.

    Article  Google Scholar 

  • Verma, D., Kainthola, A., Thareja, R., and Singh, T. N. (2013). “Stability analysis of an open cut slope in Wardha valley coal field.” Journal of the Geological Society of India, Vol. 81, No. 6, pp. 804–812, DOI: https://doi.org/10.1007/s12594-013-0105-8.

    Article  Google Scholar 

  • Wang, H., Xu, W., and Xu, R. (2005). “Slope stability evaluation using back propagation neural networks.” Engineering Geology, Vol. 80, No. 3, pp. 302–315, DOI: https://doi.org/10.1016/j.enggeo.2005.06.005.

    Article  Google Scholar 

  • Wu, X. and Kumar, V. (2009). The Top Ten algorithms in data mining, CRC Press, New York, pp. 163–175.

    Book  Google Scholar 

  • Xu, W., Xie, S., Jean-Pascal, D., Nicolas, B., and Imbert, P. (1999). “Slope stability analysis and evaluation with probabilistic artificial neural network method.” Site Investigation Science and Technology, Vol. 3, pp. 19–21.

    Google Scholar 

  • Xue, X. (2017). “Prediction of slope stability based on Hybrid PSO and LSSVM.” Journal of Computing in Civil Engineering, Vol. 31, No. 1, pp. 1–10, DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607.

    Article  Google Scholar 

  • Yan, X. and Li, X. (2011). “Bayes discriminant analysis method for predicting the stability of open pit slope.” Proceedings of the International Conference on Electric Technology and Civil Engineering (ICETCE), Lushan, China, pp. 147–150, DOI: https://doi.org/10.1109/ICETCE.2011.5776304.

    Google Scholar 

  • Zhang, H. (2004). “The optimality of naive bayes.” Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, AAAI Press, Florida, pp. 562–567.

    Google Scholar 

  • Zhang, N. L. and Guo, H. P. (2006). Introduction to Bayesian Networks, Science Press, Beijing, pp. 160–166.

    Google Scholar 

  • Zhao, H.-b. (2008). “Slope reliability analysis using a support vector machine.” Computers and Geotechnics, Vol. 35, No. 3, pp. 459–467, DOI: https://doi.org/10.1016/j.compgeo.2007.08.002.

    Article  Google Scholar 

  • Zhou, K. and Chen, Z. (2009). “Stability prediction of tailing dam slope based on neural network pattern recognition.” Proceedings of Second International Conference on Environmental and Computer Science (ICECS’09), IEEE, Dubai, the United Arab Emirates, pp. 380–383.

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Civil Engineering and Architecture, University of Jinan, Jinan, China

    Xianda Feng & Yang Sun

  2. Geotechnical Structural Engineering Research Center, Shandong University, Shandong, China

    Shuchen Li

  3. Laboratoire 3SR, University Grenoble Alpes, Grenoble, France

    Chao Yuan

  4. State Key Lab. of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China

    Peng Zeng

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  1. Xianda Feng
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  2. Shuchen Li
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  3. Chao Yuan
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  4. Peng Zeng
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  5. Yang Sun
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Correspondence to Xianda Feng.

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Feng, X., Li, S., Yuan, C. et al. Prediction of Slope Stability using Naive Bayes Classifier. KSCE J Civ Eng 22, 941–950 (2018). https://doi.org/10.1007/s12205-018-1337-3

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  • DOI: https://doi.org/10.1007/s12205-018-1337-3

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