Abstract
Through the mathematical description of landslide evolution, landslide behavior can be simply studied by the corresponding displacement time sequence. Thus, to predict landslides, a new intelligent prediction method that combines the gray system method (GM) and an evolutionary neural network (ENN) is proposed. In this method, the original displacement is divided into two components: the displacement trend and deviation sequences, which are predicted by the GM and a new ENN combined by immunized continuous ant colony optimization and modified back propagation algorithm. This new method is applied to two typical landslides in the Three Gorges Reservoir of China to show that the forecasting precision of the new method is highly accurate in these cases. For comparison with other methods (GM and ENN), the forecasting performance of the new method is analyzed in depth. The results show that the generalization prediction precision of the new method is much higher than those of the GM and ENN. Finally, the influences of the extrapolation prediction time and the prediction step for the new method are comprehensively analyzed. The results show that the lower the extrapolation prediction time and the prediction step are, the higher the extrapolation prediction precision.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aleshin Y, Torgoev I (2013) Landslide prediction based on neural network modelling. In: Margottini C (ed) Landslide science and practice. Springer-Verlag, Berlin Heidelberg, pp 311–317
Bozzano F, Cipriani I, Mazzanti P, Prestininzi A (2014) A field experiment for calibrating landslide time-of-failure prediction functions. Int J Rock Mech Min Sci 67:69–77. https://doi.org/10.1016/j.ijrmms.2013.12.006
Chen JJ, Zeng ZZ, Jiang P, Tang HM (2015a) Deformation prediction of landslide based on functional network. Neurocomputing 149:151–157. https://doi.org/10.1016/j.neucom.2013.10.044
Chen HQ, Zeng ZG (2013) Deformation prediction of landslide based on improved back-propagation neural network. Cogn Comput 5:56–62. https://doi.org/10.1007/s12559-012-9148-1
Chen HQ, Zeng ZG, Tang HM (2015b) Landslide deformation prediction based on recurrent neural network. Neural Process Lett 41:169–178. https://doi.org/10.1007/s11063-013-9318-5
Churing Y (1995) Backpropagation, theory, architecture and applications. Lawrence Erbaum Publishers, New York
Fang J, Xi YG (1997) Neural network design based on evolutionary programming. Artif Intell Eng 11:155–161. https://doi.org/10.1016/s0954-1810(96)00025-8
Feng XT, Zhang ZQ, Xu P (1999) Adaptive and intelligent prediction of deformation time series of high rock slope. Trans Nonferrous Metals Soc China 9:842–846. https://doi.org/10.1023/A:1018820401533
Fukuzono T (1990) Recent studies on time predication of slope failure. Landslide News 4:9–12
Gao W (2006) Study on displacement prediction of landslide based on grey system and evolutionary neural network. In: Yao CH, Yuan MW (eds) Computational methods in engineering and science-EPMESC X. Tsinghua University Press & Springer-Verlag, Beijing, pp 890–894
Gao W (2010) Prediction of landslide based on grey system and evolutionary artificial neural networks. In: Wu Y (ed) Proceedings of international conference on system science. Engineering Design and Manufacturing Informatization. IEEE Press, New York, pp 64–67
Gao W (2016) Displacement back analysis for underground engineering based on immunized continuous ant colony optimization. Eng Optim 48:868–882. https://doi.org/10.1080/0305215X.2015.1061814
Gao W (2018) Integrated intelligent method for displacement prediction in underground engineering. Neural Process Lett 47:1055–1075. https://doi.org/10.1007/s11063-017-9685-4
Gao W, Feng XT (2004) Study on displacement predication of landslide based on grey system and evolutionary neural network. Rock Soil Mech 25:514–517 [in Chinese]
Gao W, He TY (2017) Displacement prediction in geotechnical engineering based on evolutionary neural network. Geomech Eng 13:845–860. https://doi.org/10.12989/gae.2017.13.5.845
Gao W, Yin ZX (2011) Modern intelligent bionics algorithm and its applications. Science Press, Beijing [in Chinese]
Huang RQ (2007) Large-scale landslides and their sliding mechanisms in China since the 20th century. Chin J Rock Mech Eng 26:433–454 [in Chinese]
Huang RQ, Xu Q (1997) The principle and application of generalized system science for engineering geology. Geological Press, Beijing [in Chinese]
Huang ZQ, Law KT, Liu HD, Jiang T (2009) The chaotic characteristics of landslide evolution: a case study of Xintan landslide. Environ Geol 56:1585–1591. https://doi.org/10.1007/s00254-008-1256-6
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. https://doi.org/10.1007/s10346-013-0436-y
Jibson RW (2007) Regression models for estimating coseismic landslide displacement. Eng Geol 91:209–218. https://doi.org/10.1016/j.enggeo.2007.01.013
Kilburn CRJ, Voight B (1998) Slow rock fracture as eruption precursor at Soufriere Hills Volcano, Montserrat. Geophys Res Lett 25:3665–3668. https://doi.org/10.1029/98gl01609
Li TB, Chen MD, Wang LS (1996) Time prediction of landslides using the Verhulst inverse function model. In: Senneset K (ed) Proceedings of the 7th international symposium on landslides. A.A. Balkema Publishers, Rotterdam, pp 1289–1293
Lian C, Zeng ZG, Yao W, Tang HM (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards 66:759–771. https://doi.org/10.1007/978-3-642-34478-7_30
Liu ZB, Xu WY, Shao JF (2012) Gaussian process based approach for application on landslide displacement analysis and prediction. Comput Model Eng Sci 84:99–122. https://doi.org/10.3970/cmes.2012.084.099
Liu SF, Forrest JYL (2011) Grey systems: theory and applications. Springer, Berlin Heidelberg
Lu P, Rosenbaum MS (2003) Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 30:383–398. https://doi.org/10.1023/B:NHAZ.0000007168.00673.27
Qiao DL, Zhao M (2011) Deformation prediction based on time series analysis and grey system theory. Adv Mater Res 368-373:2147–2152. https://doi.org/10.4028/AMR.368-373.2147
Qin SQ, Jiao JJ, Wang SJ (2001) The predictable time scale of landslides. B Eng Geol Environ 59:307–312. https://doi.org/10.1007/s100640000062
Qin SQ, Jiao JJ, Wang SJ (2002) A nonlinear dynamical model of landslide evolution. Geomorphology 43:77–85. https://doi.org/10.1016/S0169-555X(01)00122-2
Qin SQ, Zhang ZY, Wang SQ, Huang RQ (1993) An introduction to nonlinear engineering geology. Southwest Jiaotong University Press, Chengdu [in Chinese]
Wang SQ (2009) Time prediction of the Xintan landslide in Xiling Gorge, the Yangtze River. In: Wang F, Li T (eds) Landslide disaster mitigation in Three Gorges Reservoir, China. Springer-Verlag, Berlin Heidelberg, pp 411–431
Wen BQ, Wang SJ, Wang EZ, Zhang JM (2004) Characteristics of rapid giant landslides in China. Landslides 1:247–261. https://doi.org/10.1007/s10346-004-0022-4
Xu F, Wang Y, Du J, Ye J (2011) Study of displacement prediction model of landslide based on time series analysis. Chin J Rock Mech Eng 30:746–751 [in Chinese]
Xue GF (2009) A study of the 1985 Xintan landslide in Xiling Gorge, Three Gorges Area, China. In: Wang F, Li T (eds) Landslide disaster mitigation in Three Gorges Reservoir, China. Springer-Verlag, Berlin Heidelberg, pp 387–409
Zhou CY, Yan TZ, Tang LS (1995) A research on non-linear dynamic method of landslide hazard system. J Changchun Univ Earth Sci 25:310–316 [in Chinese]
Zhu C, Hu G (2013) Time series prediction of landslide displacement using SVM model: application to Baishuihe landslide in Three Gorges Reservoir area, China. Appl Mech Mater 239-240:1413–1420. https://doi.org/10.4028/AMM.239-240.1413
Funding
The financial support from The Fundamental Research Funds for the Central Universities under Grant No. 2016B10214 is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gao, W., Dai, S. & Chen, X. Landslide prediction based on a combination intelligent method using the GM and ENN: two cases of landslides in the Three Gorges Reservoir, China. Landslides 17, 111–126 (2020). https://doi.org/10.1007/s10346-019-01273-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10346-019-01273-w