Abstract
Landslide is a complex geological natural disaster that brings harm or damage to human beings and their living environment. By strengthening landslide monitoring and forecasting technology, people can avoid or reduce the impact of disasters more reasonably. At present, the single step prediction of landslide displacement time series mainly uses t time to predict the data of t+1 moment, which obviously makes it difficult for people to take appropriate measures to deal with landslide changes. In this paper, a time reverse recursive algorithm based on extended Kalman filter (EKF)and Back propagation trough time (BPTT) method, is used to predict landslide displacement in order to extend the time width of landslide prediction. The EKF is firstly used to optimize the BPTT weights, and then the network parameters are adjusted in real time to improve the reliability of the prediction. Finally, the landslide displacement data of Liangshuijing (LSJ) in the three Gorges Reservoir area is used as experimental samples to verify the feasibility and practicability of EKF-BPTT.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Qin, S.Q., Jiao, J.J., Wang, S.J.: The predictable time scale of landslides. Bull. Eng. Geol. Environ. 59, 307–312 (2001)
Qin, S.Q., Jiao, J.J., Wang, S.J.: A nonlinear dynamical model of landslide evolution. Geomorphology 43, 77–85 (2002)
Chen, C.T., Lin, M.L., Wang, K.L.: Landslide seismic signal recognition and mobility for an earthquake-induced rockslide in Tsaoling, Taiwan. Eng. Geol. 171, 31–44 (2014)
Sorbino, G., Sica, C., Cascini, L.: Susceptibility analysis of shallow landslides source areas using physically based models. Nat. Hazards 53, 313–332 (2010)
Miao, H.B., Wang, G.H., Yin, K.L., Kamai, T., Lin, Y.Y.: Mechanism of the slow-moving landslides in Jurassic red-strata in the Three Gorges Reservoir, China. Eng. Geol. 171, 59–69 (2014)
Zhang, Y.B., Chen, G.Q., Zheng, L., Li, Y., Wu, J.: Effects of near-fault seismic loadings on run-out of large-scale landslide: a case study. Eng. Geol. 166, 216–236 (2013)
Inoussa, G., Peng, H., Wu, J.: Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model. Neurocomputing 86, 59–74 (2012)
Li, X.Z., Kong, J.M., Wang, Z.Y.: Landslide displacement prediction based on combining method with optimal weight. Nat. Hazards 61, 635–646 (2012)
Jakob, M.: The impacts of logging on landslide activity at Clayoquot Sound. Br. Columbia Catena 38(4), 279–300 (2000)
Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A.: Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94, 379–400 (2008)
Lajtai, E.Z., Schmidtke, R.H., Bielus, L.P.: The effect of water on the time-dependent deformation and fracture of a granite. Int. J. Rock Mech. Min. Sci. Geomech. Abs. 24(4), 247–255 (1987)
Lin, X.S., Guo, Y.: A study on coupling relationship between landslide and rainfall. J. Catastrophol. 16(2), 87–92 (2001)
Huang, R.Q., Zhao, S.J., Song, X.B.: The formation and mechanism analysis of Tiantai landslide, Xuanhan County Sichuan Province. Hydrogeol. Eng. Geol. 32(1), 13–15 (2005)
Xu, J.C., Shang, Y.Q., Wang, J.L.: Study on relationship between slope-mass slide displacement and precipitation of loose soil landslide. Chin. J. Rock Mech. Eng. 1, 2854–2860 (2006)
Herrera, G., Fcmaudez-Merodo, J.A., Mulas, J., et al.: A landslide forecasting model using ground based SAR data: the Portalet case study. Eng. Geol. 105(3–4), 220–230 (2009)
Bui, D.T., Pradhan, B., Lofman, O., et al.: Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Nat. Hazards 66(2), 1–24 (2012)
Yao, W., Zeng, Z.G., Lian, C., et al.: Ensembles of echo state networks for time series prediction. In: Proceedings of the 6th International Conference on Advanced Computational Intelligence Hangzhou, China, pp. 299–304 (2013)
Lian, C., Zeng, Z.G., Yao, W., Tang, H.M.: Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat. Hazards 66, 759–771 (2013)
Frenzel, S., Pompe, B.: Partial mutual information for coupling analysis of multivariate time series. Phys. Rev. Lett. 99, 1–4 (2007)
Acknowledgements
The work was supported by the Natural Science Foundation of China under Grants 61841301, 61603129 and 61673188, the Research Project of Hubei Provincial Department of Education under Grant Q20184504, the Scientific Research Project of Hubei PolyTechnic University under Grant 18xjz02C.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, P., Chen, J., Zeng, Z. (2019). Multi Step Prediction of Landslide Displacement Time Series Based on Extended Kalman Filter and Back Propagation Trough Time. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-22796-8_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22795-1
Online ISBN: 978-3-030-22796-8
eBook Packages: Computer ScienceComputer Science (R0)